The PREVENT dementia programme: baseline demographic, lifestyle, imaging and cognitive data from a midlife cohort study investigating risk factors for dementia.

PREVENT is a multi-centre prospective cohort study in the UK and Ireland that aims to examine midlife risk factors for dementia and identify and describe the earliest indices of disease development. The PREVENT dementia programme is one of the original epidemiological initiatives targeting midlife as a critical window for intervention in neurodegenerative conditions. This paper provides an overview of the study protocol and presents the first summary results from the initial baseline data to describe the cohort. Participants in the PREVENT cohort provide demographic data, biological samples (blood, saliva, urine and optional cerebrospinal fluid), lifestyle and psychological questionnaires, undergo a comprehensive cognitive test battery and are imaged using multi-modal 3-T MRI scanning, with both structural and functional sequences. The PREVENT cohort governance structure is described, which includes a steering committee, a scientific advisory board and core patient and public involvement groups. A number of sub-studies that supplement the main PREVENT cohort are also described. The PREVENT cohort baseline data include 700 participants recruited between 2014 and 2020 across five sites in the UK and Ireland (Cambridge, Dublin, Edinburgh, London and Oxford). At baseline, participants had a mean age of 51.2 years (range 40-59, SD ± 5.47), with the majority female (n = 433, 61.9%). There was a near equal distribution of participants with and without a parental history of dementia (51.4% versus 48.6%) and a relatively high prevalence of APOEɛ4 carriers (n = 264, 38.0%). Participants were highly educated (16.7 ± 3.44 years of education), were mainly of European Ancestry (n = 672, 95.9%) and were cognitively healthy as measured by the Addenbrookes Cognitive Examination-III (total score 95.6 ± 4.06). Mean white matter hyperintensity volume at recruitment was 2.26 ± 2.77 ml (median = 1.39 ml), with hippocampal volume being 8.15 ± 0.79 ml. There was good representation of known dementia risk factors in the cohort. The PREVENT cohort offers a novel data set to explore midlife risk factors and early signs of neurodegenerative disease. Data are available open access at no cost via the Alzheimer's Disease Data Initiative platform and Dementia Platforms UK platform pending approval of the data access request from the PREVENT steering group committee.

Enhancing stroke care in Ghana: A systematic review of stroke rehabilitation services.

OBJECTIVE: In Ghana, the shifting demographics of stroke incidence towards young adults have prompted the expansion of stroke care and rehabilitation efforts. Nevertheless, the precise impact of stroke rehabilitation remains unclear. We conducted a systematic review to explore the landscape and effects of stroke rehabilitation in Ghana. METHOD: We identified articles on stroke rehabilitation services in Ghana through searches of PubMed, Scopus, Embase, and Web of Science from inception until February 2024. The Critical Appraisal Skills Programme (CASP) Qualitative Checklist was employed to assess the risk of bias in the included studies, supplemented by qualitative synthesis. RESULTS: Among the 213 articles screened, 8 were deemed suitable for review. These studies primarily focused on two groups: stroke survivors (n = 335) and healthcare professionals (HCPs) (n = 257). Many stroke survivors reported significant benefits from telerehabilitation, with increased participation in rehabilitation activities correlating with improved physical and cognitive outcomes. The findings also underscored a lack of knowledge about stroke rehabilitation among HCPs, alongside variations in the availability of protocols and guidelines for stroke management across different hospital levels. CONCLUSIONS: The review reveals several challenges in stroke rehabilitation in Ghana, including disparities in HCPs' perceptions and utilization of rehabilitation services. The findings emphasize the need for comprehensive, patient-centered approaches, standardized training for HCPs, improved resource allocation, and the integration of telehealth to overcome barriers and enhance stroke rehabilitation in Ghana. These insights hold significance not only for Ghana but also for guiding strategies in similar contexts worldwide, aiming to improve stroke rehabilitation outcomes.

Knowledge, attitudes, and practices of caregivers on childhood immunization in Okaikoi sub-metro of Accra, Ghana.

Background: Immunization remains one of the most cost-effective health interventions. However, there are still issues of vaccine hesitancy especially in caregivers who are required to protect their children from vaccine-preventable diseases. This thwarts the overall vaccine coverage in disease-endemic areas such as sub-Saharan Africa. Therefore, to determine the factors that promote vaccine hesitancy in caregivers, this study sought to assess the knowledge, attitude, and practices of caregivers on childhood immunization in Okaikoi, a sub-metro of Accra in Ghana. Methods: A cross-sectional study on childhood immunization was conducted to determine the knowledge, attitudes, and practices of caregivers. A total of 120 caregivers with infants aged 12 months to 23 months were interviewed with a structured questionnaire containing open-ended and closed-ended queries. Results: From the community, infants whose caregivers had adhered completely to immunization constituted 53.3% while the rest were partially immunized. The two main deterrents to complete immunization were time constraints (25.8%) and forgetfulness (17.5%). It was observed that vaccination uptake and maternal level of education, as well as vaccination adverse reaction, did not impact the completion of the EPI program by these caregivers. Unfortunately, it was noted that caregivers with higher education levels were unable to complete their vaccination schedules due to their busy work schedules. Nonetheless, the main deterrent to adhering to complete childhood immunization was poor maternal knowledge (58%). Conclusion: The study revealed that, the caregivers in the community had poor knowledge on vaccination and its benefits, and therefore, with no strict adherence to vaccination schedules. This promoted the incomplete immunization of children in the community by their caregivers. Also, since the main source of information with regard to immunization in the sub-metro was through the antenatal and postnatal child welfare clinics and the media, we recommend that the health workers collaborate with media personnel to ensure that standardized information is disseminated.

Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.

Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P â€‹+ â€‹S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81-0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52-0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P â€‹+ â€‹S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Inflammatory cytokines as potential biomarkers for early diagnosis of severe malaria in children in Ghana.

BACKGROUND: Severe malaria (SM) is a fatal multi-system disease which accounted for an estimated 619,000 deaths in 2021. Less than 30% of children presenting with SM are diagnosed and treated promptly, resulting in increased mortality and neurologic impairments in survivors. Studies have identified cytokine profiles that differentiate the various clinical manifestations of malaria (severe and uncomplicated). However, the diagnostic capability of these cytokines in differentiating between the disease states in terms of cut-off values has not yet been determined. METHODS: The plasma levels of 22 pro-inflammatory cytokines (Eotaxin/CCL 11, interferon-gamma (IFN-γ), interleukin (IL)- 2, IL-6, IL-1ß, IL-12p40/p70, IL-17A, RANTES, MCP-1, IL-15, IL-5, IL-1RA, IL-2R, IFN-α, IP-10, TNF, MIG, MIP-1α, MIP-1ß, IL-7, IL-8 and Granulocyte Macrophage-Colony Stimulating Factor (GM-CSF), and 3 anti-inflammatory cytokines-(IL-4, IL-13 and IL-10) in patients with SM, uncomplicated malaria (UM) and other febrile conditions, were measured and compared using the Human Cytokine Magnetic 25-Plex Panel. The receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of these cytokines. RESULTS: The level of the pro-inflammatory cytokine, IL-17A, was significantly higher in the SM group as compared to the UM group. Levels of the anti-inflammatory cytokines however did not differ significantly among the SM and UM groups. Only IL-1ß and IL-17A showed good diagnostic potential after ROC curve analysis. CONCLUSION: The data show that levels of pro-inflammatory cytokines correlate with malaria disease severity. IL-1ß and IL-17A showed good diagnostic potentials and can be considered for use in clinical practice to target treatment.

Population cause of death estimation using verbal autopsy methods in large-scale field trials of maternal and child health: lessons learned from a 20-year research collaboration in Central Ghana.

Low and middle-income countries continue to use Verbal autopsies (VAs) as a World Health Organisation-recommended method to ascertain causes of death in settings where coverage of vital registration systems is not yet comprehensive. Whilst the adoption of VA has resulted in major improvements in estimating cause-specific mortality in many settings, well documented limitations have been identified relating to the standardisation of the processes involved. The WHO has invested significant resources into addressing concerns in some of these areas; there however remains enduring challenges particularly in operationalising VA surveys for deaths amongst women and children, challenges which have measurable impacts on the quality of data collected and on the accuracy of determining the final cause of death. In this paper we describe some of our key experiences and recommendations in conducting VAs from over two decades of evaluating seminal trials of maternal and child health interventions in rural Ghana. We focus on challenges along the entire VA pathway that can impact on the success rates of ascertaining the final cause of death, and lessons we have learned to optimise the procedures. We highlight our experiences of the value of the open history narratives in VAs and the training and skills required to optimise the quality of the information collected. We describe key issues in methods for ascertaining cause of death and argue that both automated and physician-based methods can be valid depending on the setting. We further summarise how increasingly popular information technology methods may be used to facilitate the processes described. Verbal autopsy is a vital means of increasing the coverage of accurate mortality statistics in low- and middle-income settings, however operationalisation remains problematic. The lessons we share here in conducting VAs within a long-term surveillance system in Ghana will be applicable to researchers and policymakers in many similar settings.

COVID-19 and future pandemics: A blockchain-based privacy-aware secure borderless travel solution from electronic health records.

COVID-19 pandemic undoubtedly lingers on and has brought unprecedented changes globally including travel arrangements. Blockchain-based solutions have been proposed to aid travel amid the pandemic hap. Presently, extant solutions are country or regional-based, downplay privacy, non-responsive, often impractical, and come with blockchain-related complexities presenting technological hurdle for travelers. We therefore propose a solution namely, Borderless to foster global travel allowing travelers and countries collaboratively engage in a secure adaptive proof protocol dubbed Proof-of-COVID-19 status a number of arbitrary statements to ascertain the fact that the traveler poses no danger irrespective of the country located. As far as we know, this is first of its kind. Borderless is implemented as a decentralized application leveraging blockchain as a trust anchor and decentralized storage technology. Security analysis and evaluation are performed proving security, privacy-preservation, and cost-effectiveness along with implementation envisioning it as a blueprint to facilitate cross-border travel during the present and future pandemics. Our experimental results show it takes less than 60 and 3 s to onboard users and perform proof verification respectively attesting to real usability scenarios along with the traits of arbitrary proofs to aid responsiveness to the dynamics of pandemics and blockchain abstraction from travelers.

Rational design of potent ultrashort antimicrobial peptides with programmable assembly into nanostructured hydrogels.

Microbial resistance to common antibiotics is threatening to cause the next pandemic crisis. In this context, antimicrobial peptides (AMPs) are receiving increased attention as an alternative approach to the traditional small molecule antibiotics. Here, we report the bi-functional rational design of Fmoc-peptides as both antimicrobial and hydrogelator substances. The tetrapeptide Fmoc-WWRR-NH2-termed Priscilicidin-was rationally designed for antimicrobial activity and molecular self-assembly into nanostructured hydrogels. Molecular dynamics simulations predicted Priscilicidin to assemble in water into small oligomers and nanofibrils, through a balance of aromatic stacking, amphiphilicity and electrostatic repulsion. Antimicrobial activity prediction databases supported a strong antimicrobial motif via sequence analogy. Experimentally, this ultrashort sequence showed a remarkable hydrogel forming capacity, combined to a potent antibacterial and antifungal activity, including against multidrug resistant strains. Using a set of biophysical and microbiology techniques, the peptide was shown to self-assemble into viscoelastic hydrogels, as a result of assembly into nanostructured hexagonal mesophases. To further test the molecular design approach, the Priscilicidin sequence was modified to include a proline turn-Fmoc-WPWRR-NH2, termed P-Priscilicidin-expected to disrupt the supramolecular assembly into nanofibrils, while predicted to retain antimicrobial activity. Experiments showed P-Priscilicidin self-assembly to be effectively hindered by the presence of a proline turn, resulting in liquid samples of low viscosity. However, assembly into small oligomers and nanofibril precursors were evidenced. Our results augur well for fast, adaptable, and cost-efficient antimicrobial peptide design with programmable physicochemical properties.

Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease.

Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.

Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms.

Alzheimer's disease (AD) has its onset many decades before dementia develops, and work is ongoing to characterise individuals at risk of decline on the basis of early detection through biomarker and cognitive testing as well as the presence/absence of identified risk factors. Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results. However, these approaches have been criticised as they are unable to generalise due to over-reliance on one data source, poor internal and external validations, and lack of understanding of prediction models, thereby limiting the clinical utility of these prediction models. We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia. Our prediction models, known as source models, are initially trained and tested using a publicly available dataset (n = 84,856, mean age = 69 years) with 14 years of follow-up samples to predict the individual risk of developing dementia. The decision boundaries of the best source model are further updated by using an alternative dataset from a different and much younger population (n = 473, mean age = 52 years) to obtain an additional prediction model known as the target model. We further apply the SHapely Additive exPlanation (SHAP) algorithm to visualise the risk factors responsible for the prediction at both population and individual levels. The best source model achieves a geometric accuracy of 87%, specificity of 99%, and sensitivity of 76%. In comparison to a baseline model, our target model achieves better performance across several performance metrics, within an increase in geometric accuracy of 16.9%, specificity of 2.7%, and sensitivity of 19.1%, an area under the receiver operating curve (AUROC) of 11% and a transfer learning efficacy rate of 20.6%. The strength of our approach is the large sample size used in training the source model, transferring and applying the "knowledge" to another dataset from a different and undiagnosed population for the early detection and prediction of dementia risk, and the ability to visualise the interaction of the risk factors that drive the prediction. This approach has direct clinical utility.

Human neuropeptide substance P self-assembles into semi-flexible nanotubes that can be manipulated for nanotechnology.

Substance P neuropeptide is here reported to self-assemble into well-defined semi-flexible nanotubes. Using a blend of synchrotron small angle X-ray scattering, atomic force microscopy and other biophysical techniques, the natural peptide is shown to self-assemble into monodisperse 6 nm wide nanotubes, which can closely associate into nano-arrays with nematic properties. Using simple protocols, the nanotubes could be precipitated or mineralised while conserving their dimensions and core-shell morphology. Our discovery expands the small number of available monodisperse peptide nanotube systems for nanotechnology, beyond direct relevance to biologically functional peptide nanostructures since the substance P nanotubes are fundamentally different from typical amyloid fibrils.

Trauma and depressive symptomatology in middle-aged persons at high risk of dementia: the PREVENT Dementia Study.

OBJECTIVE: Depression and trauma are associated with changes in brain regions implicated in Alzheimer's disease. The present study examined associations between childhood trauma, depression, adult cognitive functioning and risk of dementia. METHODS: Data from 378 participants in the PREVENT Dementia Study aged 40-59 years. Linear and logistic models were used to assess associations between childhood trauma, depression, dementia risk, cognitive test scores and hippocampal volume. RESULTS: Childhood trauma was associated with depression and reduced hippocampal volume but not current cognitive function or dementia risk. Poorer performance on a delayed face/name recall task was associated with depression. Childhood trauma was associated with lower hippocampal volume however poorer cognitive performance was mediated by depression rather than structural brain differences. CONCLUSION: Depressive symptomatology may be associated with dementia risk via multiple pathways, and future studies should consider subtypes of depressive symptomatology when examining its relationship to dementia.

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.

Statistical methods for dementia risk prediction and recommendations for future work: A systematic review.

INTRODUCTION: Numerous dementia risk prediction models have been developed in the past decade. However, methodological limitations of the analytical tools used may hamper their ability to generate reliable dementia risk scores. We aim to review the used methodologies. METHODS: We systematically reviewed the literature from March 2014 to September 2018 for publications presenting a dementia risk prediction model. We critically discuss the analytical techniques used in the literature. RESULTS: In total 137 publications were included in the qualitative synthesis. Three techniques were identified as the most commonly used methodologies: machine learning, logistic regression, and Cox regression. DISCUSSION: We identified three major methodological weaknesses: (1) over-reliance on one data source, (2) poor verification of statistical assumptions of Cox and logistic regression, and (3) lack of validation. The use of larger and more diverse data sets is recommended. Assumptions should be tested thoroughly, and actions should be taken if deviations are detected.

Does facility birth reduce maternal and perinatal mortality in Brong Ahafo, Ghana? A secondary analysis using data on 119 244 pregnancies from two cluster-randomised controlled trials.

BACKGROUND: Maternal and perinatal mortality are still unacceptably high in many countries despite steep increases in facility birth. The evidence that childbirth in facilities reduces mortality is weak, mainly because of the scarcity of robust study designs and data. We aimed to assess this link by quantifying the influence of major determinants of facility birth (cluster-level facility birth, wealth, education, and distance to childbirth care) on several mortality outcomes, while also considering quality of care. METHODS: Our study is a secondary analysis of surveillance data on 119 244 pregnancies from two large population-based cluster-randomised controlled trials in Brong Ahafo, Ghana. In addition, we specifically collected data to assess quality of care at all 64 childbirth facilities in the study area. Outcomes were direct maternal mortality, perinatal mortality, first-day and early neonatal mortality, and antepartum and intrapartum stillbirth. We calculated cluster-level facility birth as the percentage of facility births in a woman's village over the preceding 2 years, and we computed distances from women's regular residence to health facilities in a geospatial database. Associations between determinants of facility birth and mortality outcomes were assessed in crude and multivariable multilevel logistic regression models. We stratified perinatal mortality effects by three policy periods, using April 1, 2005, and July 1, 2008, as cutoff points, when delivery-fee exemption and free health insurance were introduced in Ghana. These policies increased facility birth and potentially reduced quality of care. FINDINGS: Higher proportions of facility births in a cluster were not linked to reductions in any of the mortality outcomes. In women who were wealthier, facility births were much more common than in those who were poorer, but mortality was not lower among them or their babies. Women with higher education had lower mortality risks than less-educated women, except first-day and early neonatal mortality. A substantially higher proportion of women living in areas closer to childbirth facilities had facility births and caesarean sections than women living further from childbirth facilities, but mortality risks were not lower despite this increased service use. Among women who lived in areas closer to facilities offering comprehensive emergency obstetric care (CEmOC), emergency newborn care, or high-quality routine care, or to facilities that had providers with satisfactory competence, we found a lower risk of intrapartum stillbirth (14·2 per 1000 deliveries at >20 km from a CEmOC facility vs 10·4 per 1000 deliveries at ≤1 km; odds ratio [OR] 1·13, 95% CI 1·06-1·21) and of composite mortality outcomes than among women living in areas where these services were further away. Protective effects of facility birth were restricted to the two earlier policy periods (from June 1, 2003, to June 30, 2008), whereas there was evidence for higher perinatal mortality with increasing wealth (OR 1·09, 1·03-1·14) and lower perinatal mortality with increasing distance from childbirth facilities (OR 0·93, 0·89-0·98) after free health insurance was introduced in July 1, 2008. INTERPRETATION: Facility birth does not necessarily convey a survival benefit for women or babies and should only be recommended in facilities capable of providing emergency obstetric and newborn care and capable of safe-guarding uncomplicated births. FUNDING: The Baden-Württemberg Foundation, the Daimler and Benz Foundation, the European Social Fund and Ministry of Science, Research, and the Arts Baden-Württemberg, WHO, US Agency for International Development, Save the Children, the Bill & Melinda Gates Foundation, and the UK Department for International Development.