Future Disease Research Articles

MiR-26a is a Key Therapeutic Target with Enormous Potential in the Diagnosis and Prognosis of Human Disease.

MicroRNA-26a (miR-26a) belongs to small non-coding regulatory RNA molecules emerging as fundamental post-transcriptional regulators inhibiting gene expression that plays vital roles in various processes of human diseases such as depression, renal ischemia and reperfusion injury, liver injury and some refractory cancer. In this review, we expound on the results of studies about miR-26a with emphasis on its function in animal models or in vitro cell culture to simulate the most common human disease in the clinic. Furthermore, we also illustrate the underlying mechanisms of miR-26a in strengthening the antitumor activity of antineoplastic drugs. Importantly, dysregulation of miR-26a has been related to many chronic and malignant diseases, especially in neurological disorders in the brain such as depression and neurodegenerative diseases as well as cancers such as papillary thyroid carcinoma, hepatocellular carcinoma and so on. It follows that miR-26a has a strong possibility to be a potential therapeutic target for the treatment of neurological disorders and cancers. Although the research of miRNAs has made great progress in the last few decades, much is yet to be discovered, especially regarding their underlying mechanisms and roles in the complex diseases of humans. Consequently, miR-26a has been analyzed in chronic and malignant diseases, and we discuss the dysregulation of miR-26a and functional roles in the development and pathogenesis of these diseases, which is very helpful for understanding their mechanisms as new biomarkers for diagnosing and curing diseases in the near future.

Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT)

BackgroundPredicting chronic disease incidence at a population level can help inform overall future chronic disease burden and opportunities for prevention. This study aimed to estimate the future burden of chronic...

Heart Disease Prediction Using Machine Learning

Over the past few decades, heart-related diseases, or cardiovascular diseases (CVDs) have emerged as the leading cause of death not only in India but worldwide. They are the main cause of many diseases in the world. Machine learning algorithms and techniques have been applied to various clinical trials to automate the analysis of large and complex data. Recently, many scientists have been using AI techniques to support life and experts to detect heart-related diseases in the care industry. Compared to the brain, which is the largest organ in the human body, the heart is the next largest organ. Blood is pumped and sent to all the organs of the body. Cardiovascular disease prediction plays an important role in clinical practice. Informative studies will be useful in anticipating more data that can help predict various diseases and focus on treatment. There is a lot of data about the patients that are seen each month. The stored data can be a source for predicting future disease outbreaks. Some methods of data mining and artificial intelligence are used to predict heart diseases, such as artificial neural networks (ANN), animation, etc. To reduce the number of people who die from heart disease, we need to do it quickly and have a way to detect it. Data mining techniques and artificial intelligence calculations help experts make better cancer predictions and diagnoses. The main goal of this research project is to use AI statistics to predict coronary heart disease in patients.

Long-Term Glucocorticoid Exposure and Incident Cardiovascular Diseases-The Lifelines Cohort.

Long-term glucocorticoid levels in scalp hair (HairGCs), including cortisol and the inactive form cortisone, represent the cumulative systemic exposure to glucocorticoids over months. HairGCs have repeatedly shown associations with cardiometabolic and immune parameters, but longitudinal data are lacking. We investigated 6341 hair samples of participants from the Lifelines cohort study for cortisol and cortisone levels and associated these to incident cardiovascular diseases (CVD) during 5 to 7 years of follow-up. We computed the odds ratio (OR) of HairGC levels for incident CVD via logistic regression, adjusting for classical cardiovascular risk factors, and performed a sensitivity analysis in subcohorts of participants < 60 years and ≥ 60 years of age. We also associated HairGC levels to immune parameters (total leukocytes and subtypes). Hair cortisone levels (available in n = 4701) were independently associated with incident CVD (P < .001), particularly in younger individuals (multivariate-adjusted OR 4.21, 95% CI 1.91-9.07 per point increase in 10-log cortisone concentration [pg/mg], P < .001). All immune parameters except eosinophils were associated with hair cortisone (all multivariate-adjusted P < .05). In this large, prospective cohort study, we found that long-term cortisone levels, measured in scalp hair, represent a relevant and significant predictor for future CVD in younger individuals. These results highlight glucocorticoid action as possible treatment target for CVD prevention, where hair glucocorticoid measurements could help identify individuals that may benefit from such treatments.

Influence of COVID-19 on Consumer Preference for Turfgrass Attributes in the Southern United States

This study estimates the influence of the coronavirus disease 2019 (COVID-19) pandemic on consumer preferences for turfgrass attributes by analyzing data from two surveys conducted in Jan 2019 and Apr 2021. First, the study estimated a mixed logit model to account for individual heterogeneity in preferences. Subsequently, estimates of the willingness to pay (WTP) were compared between periods before and after the pandemic. To show the impact of consumers’ risk attitudes with respect to climate change on their preference for turfgrass attributes, we re-estimated the model according to the risk attitude groups (i.e., risk-seeking vs. risk-averse). Finally, to examine how consumers’ demographic characteristics and risk attitudes are related to their WTP for improved turfgrass attributes, we estimated a random-effect panel data model for each attribute. Our results showed that, overall, consumers’ WTP increased during the COVID-19 pandemic. We also found that the WTP of risk-averse consumers were mostly higher than those of risk-seeking consumers during both time periods. Furthermore, the increase in the WTP observed among the risk-averse group was greater than the increase of the WTP of the risk-seeking group. Our findings imply that the demand for drought-tolerant and stress-resistant turfgrasses would increase with possible future climate changes and infectious disease outbreaks.

In vitro competition between two transmissible cancers and potential implications for their host, the Tasmanian devil.

Since the emergence of a transmissible cancer, devil facial tumour disease (DFT1), in the 1980s, wild Tasmanian devil populations have been in decline. In 2016, a second, independently evolved transmissible cancer (DFT2) was discovered raising concerns for survival of the host species. Here, we applied experimental and modelling frameworks to examine competition dynamics between the two transmissible cancers invitro. Using representative cell lines for DFT1 and DFT2, we have found that in monoculture, DFT2 grows twice as fast as DFT1 but reaches lower maximum cell densities. Using co-cultures, we demonstrate that DFT2 outcompetes DFT1: the number of DFT1 cells decreasing over time, never reaching exponential growth. This phenomenon could not be replicated when cells were grown separated by a semi-permeable membrane, consistent with exertion of mechanical stress on DFT1 cells by DFT2. A logistic model and a Lotka-Volterra competition model were used to interrogate monoculture and co-culture growth curves, respectively, suggesting DFT2 is a better competitor than DFT1, but also showing that competition outcomes might depend on the initial number of cells, at least in the laboratory. We provide theories how the invitro results could be translated to observations in the wild and propose that these results may indicate that although DFT2 is currently in a smaller geographic area than DFT1, it could have the potential to outcompete DFT1. Furthermore, we provide a framework for improving the parameterization of epidemiological models applied to these cancer lineages, which will inform future disease management.

"It's no use saying it in English": A qualitative study exploring community leaders' perceptions of the challenges and opportunities with translating and interpreting COVID-19 related public health messaging to reach ethnic minorities in Australia.

The Australian Government implemented a range of public health response strategies and communication approaches to reduce the spread of COVID-19; however, concerns have been raised around a failure to sufficiently consider culturally and linguistically diverse (CaLD) communities in these processes. This research aimed to understand the factors that have impacted COVID-19 communication and engagement efforts during the pandemic from the perspective of key CaLD community and faith-based leaders. A further aim was to understand the processes that could be adopted to support future communication strategies, including promoting pandemic-related vaccines. This study included 29 key informant interviews with community and faith-based leaders in New South Wales, Australia. The overwhelming message from community leaders was a sense of shared responsibility between their organisations and governments in communicating pertinent and accurate COVID-19 related information to CaLD communities. They expressed a sense of duty to keep their community members safe. However, community leaders and others shouldered significant costs related to resources and time that need to be acknowledged by governments in preparing for future disease outbreaks. They felt that governments should consider: 1) improving communication between governments and CaLD organisations; 2) responding to the specific CaLD needs with greater agility; 3) foregrounding social media in their communication strategy; 4) reinvesting in local public health units to know their population; 5) developing a health ambassadors model program; 6) preparing a hybrid model of translators/interpreters to fill the gap; and, 7) reimagining vaccine information campaigns to target CaLD communities better. Given the technical details about the COVID-19 virus conveyed in government information campaigns and the media, ensuring the most vulnerable populations, including people from CaLD backgrounds, access clear, concise and timely public health messaging from governments and community organisations requires further attention.

The malarial blood transcriptome: translational applications.

The blood transcriptome of malaria patients has been used extensively to elucidate the pathophysiological mechanisms and host immune responses to disease, identify candidate diagnostic and prognostic biomarkers, and reveal new therapeutic targets for drug discovery. This review gives a high-level overview of the three main translational applications of these studies (diagnostics, prognostics, and therapeutics) by summarising recent literature and outlining the main limitations and future directions of each application. It highlights the need for consistent and accurate definitions of disease states and subject groups and discusses how prognostic studies must distinguish clearly between analyses that attempt to predict future disease states and those which attempt to discriminate between current disease states (classification). Lastly it examines how many promising therapeutics fail due to the choice of imperfect animal models for pre-clinical testing and lack of appropriate validation studies in humans, and how future transcriptional studies may be utilised to overcome some of these limitations.

Preparing healthcare facilities in sub-Saharan Africa for future outbreaks: insights from a multi-country digital self-assessment of COVID-19 preparedness

BackgroundDespite previous experience with epidemics, African healthcare systems were inadequately prepared and substantially impacted by the coronavirus disease 2019 (COVID-19) pandemic. Limited information about the level of COVID-19 preparedness of healthcare facilities in Africa hampers policy decision-making to fight future outbreaks in the region, while maintaining essential healthcare services running.MethodsBetween May–November 2020, we performed a survey study with SafeCare4Covid − a free digital self-assessment application − to evaluate the COVID-19 preparedness of healthcare facilities in Africa following World Health Organization guidelines. The tool assessed (i) COVID-19-related capabilities with 31 questions; and (ii) availability of essential medical supplies with a 23-supplies checklist. Tailored quality improvement plans were provided after assessments. Information about facilities’ location, type, and ownership was also collected.ResultsFour hundred seventy-one facilities in 11 African countries completed the capability assessment; 412 also completed the supplies checklist. The average capability score on a scale of 0–100 (n=471) was 58.0 (interquartile range 40.0–76.0), and the average supplies score (n=412) was 61.6 (39.0–83.0). Both scores were significantly lower in rural (capability score, mean 53.6 [95%CI:50.3–57.0]/supplies score, 59.1 [55.5–62.8]) versus urban facilities (capability score, 65.2 [61.7–68.7]/supplies score, 70.7 [67.2–74.1]) (P<0.0001 for both comparisons). Likewise, lower scores were found for public versus private clinics, and for primary healthcare centres versus hospitals. Guidelines for triage and isolation, clinical management of COVID-19, staff mental support, and contact tracing forms were largely missing. Handwashing stations were partially equipped in 33% of facilities. The most missing medical supply was COVID-19 specimen collection material (71%), while 43% of facilities did not have N95/FFP2 respirators and 19% lacked medical masks.ConclusionsA large proportion of public and private African facilities providing basic healthcare in rural areas, lacked fundamental COVID-19-related capabilities and life-saving personal protective equipment. Decentralization of epidemic preparedness efforts in these settings is warranted to protect healthcare workers and patients alike in future epidemics. Digital tools are of great value to timely measure and improve epidemic preparedness of healthcare facilities, inform decision-making, create a more stakeholder-broad approach and increase health-system resilience for future disease outbreaks.

Drought: A context-dependent damper and aggravator of plant diseases.

Drought dynamically influences the interactions between plants and pathogens, thereby affecting disease outbreaks. Understanding the intricate mechanistic aspects of the multiscale interactions among plants, pathogens, and the environment-known as the disease triangle-is paramount for enhancing the climate resilience of crop plants. In this review, we systematically compile and comprehensively analyse current knowledge on the influence of drought on the severity of plant diseases. We emphasise that studying these stresses in isolation is not sufficient to predict how plants respond to combined stress from both drought and pathogens. The impact of drought and pathogens on plants is complex and multifaceted, encompassing the activation of antagonistic signalling cascades in response to stress factors. The nature, intensity, and temporality of drought and pathogen stress occurrence significantly influence the outcome of diseases. We delineate the drought-sensitive nodes of plant immunity and highlight the emerging points of crosstalk between drought and defence signalling under combined stress. The limited mechanistic understanding of these interactions is acknowledged as a key research gap in this area. The information synthesised herein will be crucial for crafting strategies for the accurate prediction and mitigation of future crop disease risks, particularly in the context of a changing climate.

Addressing Technical and Environmental Obstacles in Livestock Management for Enhanced Drinking Water Quality

Water is a vital nutrient for livestock, constituting 60 to 70 percent of their body composition. It plays a crucial role in maintaining body fluids, ion balance, digestion, nutrient absorption, waste elimination, and temperature regulation. Additionally, water serves as a lubricant and cushion for joints, spinal cord, and tissues, ranking second only to oxygen in essentiality for life. Despite its importance, water can also serve as a vehicle for transmitting pathogenic organisms, posing a significant concern for livestock health. Access to safe water remains a challenge, as the cost is prohibitive for many, leading to reliance on available water sources regardless of their quality. Given the presence of pathogenic microorganisms in freshwater bodies, routine assessment is essential to prevent future disease outbreaks among livestock populations. Keywords: Livestock Management, Drinking Water Quality, Pathogenic Organisms, Freshwater Bodies, Disease Outbreaks

Marburg virus in Tanzania: examining emergence, consequences, and mitigation approaches

The first-ever Marburg virus disease (MVD) outbreak in Tanzania serves as a stark warning of the vulnerability of the entire world to newly emerging infectious diseases. This article looks at the causes of Tanzania’s susceptibility to MVD, containment efforts, and strategies to stop further outbreaks. The analysis emphasizes the significance of addressing socio-economic variables, environmental issues, and community participation in addition to healthcare infrastructure and surveillance systems for long-term health security. It highlights the requirement for a concerted effort on the part of governments, international organizations, and local communities, as well as ongoing financial support for public health infrastructure. The opinion also urges regional collaboration and coordination, as well as the adoption of a learning attitude to incorporate lessons from this and other outbreaks for enhancing and perfecting public health policies. We can contribute to global efforts to prevent and limit future infectious disease outbreaks and protect the health of communities around the world by adopting a thorough, proactive, and evidence-based strategy.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2’s propensity for asymptomatic transmission, raise the question “how reliable was contact tracing for COVID-19 in the United States”? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Developmental changes in lung function of mice are independent of sex as a biological variable.

Pulmonary function testing (PFT) in mice includes biomechanical assessment of lung function relevant to physiology in health and its alteration in disease, hence, it is frequently used in preclinical modeling of human lung pathologies. Despite numerous reports of PFT in mice of various ages, there is a lack of reference data for developing mice collected using consistent methods. Therefore, we profiled PFTs in male and female C57BL/6J mice from 2 to 23 wk of age, providing reference values for age- and sex-dependent changes in mouse lung biomechanics during development and young adulthood. Although males and females have similar weights at birth, females weigh significantly less than males after 5 wk of age (P < 0.001) with largest weight gain observed between 3 and 8 wk in females and 3 and 13 wk in males, after which weight continued to increase more slowly up to 23 wk of age. Lung function parameters including static compliance and inspiratory capacity also increased rapidly between 3 and 8 wk in female and male mice, with male mice having significantly greater static compliance and inspiratory capacity than female mice (P < 0.001). Although these parameters appear higher in males at a given age, allometric scaling showed that static compliance and inspiratory compliance were comparable between the two sexes. This suggests that differences in measurements of lung function are likely body weight-based rather than sex-based. We expect these data to facilitate future lung disease research by filling a critical knowledge gap in our field.NEW & NOTEWORTHY This study provides reference values for changes in mouse lung biomechanics from 2 to 23 wk of age. There are rapid developmental changes in lung structure and function of male and female mice between the ages of 3 and 8 wk. Male mice become noticeably heavier than female mice at or about 5 wk of age. We identified that differences in normal lung function measurements are likely weight-based, not sex-based.

Lack of Data Access, but Not Availability, Hinders AI Training for High-Risk Conditions in Ontario.

Advanced disease prediction is an important step toward achieving a proactive healthcare system. New technologies such as artificial intelligence are very promising in their ability to predict the onset of future disease much earlier than has been possible in the past. However, artificial intelligence requires training and training requires data. In this study, we report on the ready availability, but lack of accessibility and real-time access to healthcare data required to treat five high-cost diseases that are predictable using AI and preventable using well-established evidence-based therapies. There is urgent need for action on the part of governments and other interest holders to define and invest in the infrastructure required to make data for training and deploying AI at scale more accessible.

Hierarchical Nanobiosensors at the End of the SARS-CoV-2 Pandemic.

Nanostructures have played a key role in the development of different techniques to attack severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Some applications include masks, vaccines, and biosensors. The latter are of great interest for detecting diseases since some of their features allowed us to find specific markers in secretion samples such as saliva, blood, and even tears. Herein, we highlight how hierarchical nanoparticles integrated into two or more low-dimensional materials present outstanding advantages that are attractive for photonic biosensing using their nanoscale functions. The potential of nanohybrids with their superlative mechanical characteristics together with their optical and optoelectronic properties is discussed. The progress in the scientific research focused on using nanoparticles for biosensing a variety of viruses has become a medical milestone in recent years, and has laid the groundwork for future disease treatments. This perspective analyzes the crucial information about the use of hierarchical nanostructures in biosensing for the prevention, treatment, and mitigation of SARS-CoV-2 effects.

A COMPREHENSIVE ANALYSIS OF BEEJA VIKRUTI IN GARBHA: INSIGHTS FROM TRADITIONAL INDIAN MEDICINE

Introduction: Ayurveda, an ancient system of medicine, elucidates the process of fetal development, emphasis-ing the union of sperm, ovum, and soul to form the fetus. Despite this intricate process, the potential for fetal defects, termed Garbha Vikruti, exists due to factors such as vitiation in sperm or ovum. Ayurveda attributes these defects to the mother's pre-conception and gestational diet, lifestyle, teratologic agents, and Dosha vitia-tion, impacting organogenesis and leading to diverse forms of Garbha Vikruti. The severity and timing of Dosha vitiation during pregnancy are critical in determining the extent of abnormalities, influencing fetal health and longevity. Objectives: This study explores the concepts of Garbh Vikruti and teratogenicity in Ayurveda. The research delves into the correlation between Ayurveda's Avayava Utpatti and Modern Organogenesis, examining sources including classical Ayurveda texts, journal articles, textbooks on embryology, and evidence-based re-search articles. Materials and Methods: The study synthesises information from classical Ayurveda texts (Charaka Samhita, Sushruta Samhita, Kashyapa Samhita), Ayurveda journal articles, embryology/anatomy text-books, and research articles from reputable databases. The correlation between Ayurvedic and modern concepts is analysed to understand the factors causing fetal defects. Results: The article explores Ayurvedic concepts such as Beeja (gametes), Beejabhaga (chromosomes), Beejabhagavayava (genes), Aatmakarma (spiritual influ-ences), Aashaya (uterine abnormalities), Kaala Dosha (temporal influences), and Matuaaharvihara (diet and lifestyle) as factors contributing to Garbha Vikruti. The study discusses the role of vitiated Beeja, chromosomal anomalies, genetic diseases, spiritual influences, uterine abnormalities, temporal factors, maternal diet and life-style in causing teratological malformations. Conclusion: Fetal defects are categorized into genetic (Beeja Dosha) and metabolic (Garbha Vighatakara Bha-va) factors. Genetic defects include conditions like cleft lip and congenital heart defects, while metabolic defects encompass neural tube defects. The study emphasizes the need for a holistic approach to understanding and managing these factors, ensuring optimal fetal health and preventing future diseases. Prenatal advisors are urged to discourage the consumption of certain harmful foods, contributing to a comprehensive strategy for fetal well-being and future health.

Circulating Leukocyte Subsets Before and After a Breast Cancer Diagnosis and Therapy

Changes in leukocyte composition often precede chronic disease onset. Patients with a history of breast cancer (hereinafter referred to as breast cancer survivors) are at increased risk for subsequent chronic diseases, but the long-term changes in peripheral leukocyte composition following a breast cancer diagnosis and treatment remain unknown. To examine longitudinal changes in peripheral leukocyte composition in women who did and did not develop breast cancer and identify whether differences in breast cancer survivors were associated with specific treatments. In this prospective cohort study, paired blood samples were collected from 2315 women enrolled in The Sister Study, a US-nationwide prospective cohort study of 50 884 women, at baseline (July 2003 to March 2009) and follow-up (October 2013 to March 2015) home visits, with a mean (SD) follow-up interval of 7.6 (1.4) years. By design, approximately half of the included women had been diagnosed and treated for breast cancer after enrollment and before the second blood draw. A total of 410 women were included in the present study, including 185 breast cancer survivors and 225 who remained free of breast cancer over a comparable follow-up period. Data were analyzed from April 21 to September 9, 2022. Breast cancer status and, among breast cancer survivors, cancer treatment type (chemotherapy, radiotherapy, endocrine therapy, or surgery). Blood DNA methylation data were generated in 2019 using a genome-wide methylation screening tool and deconvolved to estimate percentages of 12 circulating leukocyte subsets. Of the 410 women included in the analysis, the mean (SD) age at enrollment was 56 (9) years. Compared with breast cancer-free women, breast cancer survivors had decreased percentages of circulating eosinophils (-0.45% [95% CI, -0.87% to -0.03%]; P = .03), total CD4+ helper T cells (-1.50% [95% CI, -2.56% to -0.44%]; P = .01), and memory B cells (-0.22% [95% CI, -0.34% to -0.09%]; P = .001) and increased percentages of circulating naive B cells (0.46% [95% CI, 0.17%-0.75%]; P = .002). In breast cancer survivor-only analyses, radiotherapy was associated with decreases in total CD4+ T cell levels, whereas chemotherapy was associated with increases in naive B cell levels. Surgery and endocrine therapy were not meaningfully associated with leukocyte changes. In this cohort study of 410 women, breast cancer survivors experienced lasting changes in peripheral leukocyte composition compared with women who remained free of breast cancer. These changes may be related to treatment with chemotherapy or radiotherapy and could influence future chronic disease risk.

Predictive value of individual serum neurofilament light chain levels in short-term disease activity in relapsing multiple sclerosis.

The assessment of serum neurofilament light chain (sNFL) has emerged as a diagnostic and prognostic tool in monitoring multiple sclerosis (MS). However, the application of periodic measurement in daily practice remains unclear. To evaluate the predictive value of individual sNFL levels in determining disease activity in patients with relapsing MS (RMS). In this two-year prospective study, 129 RMS patients underwent quarterly sNFL assessments and annual MRI scans. The study analyzed the correlation between individual NFL levels and past, current, and future disease activity. Group-level Z-scores were employed as a comparative measure. Among the 37 participants, a total of 61 episodes of disease activity were observed. sNFL levels proved valuable in distinct ways; they were confirmatory of previous and current clinical and/or radiological activity and demonstrated a high negative predictive value for future 90 days activity. Interestingly, Z-scores marginally outperformed sNFL levels in terms of predictive accuracy, indicating the potential for alternative approaches in disease activity assessment. In our cohort, sNFL cut-offs of 10.8 pg./mL (sensitivity 27%, specificity 90%) and 14.3 pg./mL (sensitivity 15%, specificity 95%) correctly identified 7 and 4 out of 26 cases of radiological activity within 90 days, respectively, with 14 and 15% false negatives. When using lower cut-off values, individuals with sNFL levels below 5 pg/mL (with a sensitivity of 92%, specificity of 25%, and negative predictive value of 94%) were less likely to experience radiological activity within the next 3 months. Individual sNFL levels may potentially confirm prior or current disease activity and predict short-term future radiological activity in RMS. These findings underscore its periodic measurement as a valuable tool in RMS management and decision-making, enhancing the precision of clinical evaluation in routine practice.

Exploring the Impact of Mobile Augmented Reality on COVID-19 Prevention Education in Primary Schools

This study aimed to evaluate the effectiveness of an interactive mobile Augmented Reality (AR) game to increase the knowledge about COVID-19 prevention primary school students. We tested the application for usability and effectiveness through pre- and post-tests, questionnaires, and interviews. 12 participants from four states in Malaysia took part in the study. Their average age is 9.5 years old. Results indicated a significant improvement in student performance from the pre-test to the post-test, with a mean score has increased from 3.67 to 8.25. The average System Usability Scale (SUS) score was 75%. These findings show the effectiveness of our mobile AR application as a tool to increase the knowledge about COVID-19 prevention among primary school. The findings of this study contribute to the body of research on the use of AR in COVID-19 prevention education among primary school students. This study provides both theoretical and practical implications for educators, researcher and policymakers seeking to use mobile AR to support the prevention education of any future pandemic or infectious disease.