Real-world Settings Research Articles

Enabling computing students to recognize acquired competencies with ePortfolios

ABSTRACT Background and Context While higher education in Norway intends to ensure students can translate academic learning to real-world settings, a gap persists in computing students’ perceptions of their acquired competencies. Objective This study aims to understand the gap and explore how formative assessment with ePortfolios can help students recognize acquired competencies. Method This study investigates data from a national student survey, summative reflections from 67 computing students’ exams at the end of a course, and observations of their ePortfolio development during the course. Findings The ePortfolio emerges as a powerful tool, enabling students to actively record their learning journey, recognize acquired competencies, and gain a holistic understanding of the total learning outcomes, thus bridging the identified gap. Implications This study offers insights into how ePortfolios can help computing students recognize acquired competencies and present implications and recommendations for higher education institutions and teachers.

Molnupiravir Use Among Patients with COVID-19 in Real-World Settings: A Systematic Literature Review.

Molnupiravir (MOV) is an oral antiviral for the treatment of individuals with mild-to-moderate COVID-19 and at high risk of progression to severe disease. Our objective was to conduct a systematic literature review (SLR) of evidence on the effectiveness of MOV in reducing the risk of severe COVID-19 outcomes in real-world outpatient settings. The SLR was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and using pre-determined population, intervention, comparison, outcome, time, and study design inclusion criteria. Eligible studies were published between January 1, 2021, and March 10, 2023, and evaluated the real-world effectiveness of MOV compared to no treatment in reducing the risk of severe COVID-19 outcomes among outpatients ≥ 18years of age with a laboratory-confirmed diagnosis of SARS-CoV-2 infection. Nine studies from five countries were included in the review. The size of the MOV-treated group ranged from 359 to 7818 individuals. Omicron variants of SARS-CoV-2 were dominant in all study periods. Most studies noted differences in the baseline characteristics of the MOV-treated and untreated control groups, with the treated groups generally being older and with more comorbidities. Eight studies reported that treatment with MOV was associated with a significantly reduced risk of at least one severe COVID-19 outcome in at least one age group, with greater benefits consistently observed among older age groups. In this SLR study, treatment with MOV was effective in reducing the risk of severe outcomes from COVID-19 caused by Omicron variants, especially for older individuals. Differences in the ages and baseline comorbidities of the MOV-treated and control groups may have led to underestimation of the effectiveness of MOV in many observational studies. Real-world studies published to date thus provide additional evidence supporting the continued benefits of MOV in non-hospitalized adults with COVID-19.

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera

Background Diabetic retinopathy (DR) is a common complication of diabetes and may lead to irreversible visual loss. Efficient screening and improved treatment of both diabetes and DR have amended visual prognosis for DR. The number of patients with diabetes is increasing and telemedicine, mobile handheld devices and automated solutions may alleviate the burden for healthcare. We compared the performance of 21 artificial intelligence (AI) algorithms for referable DR screening in datasets taken by handheld Optomed Aurora fundus camera in a real-world setting. Patients and methods Prospective study of 156 patients (312 eyes) attending DR screening and follow-up. Both papilla- and macula-centred 50° fundus images were taken from each eye. DR was graded by experienced ophthalmologists and 21 AI algorithms. Results Most eyes, 183 out of 312 (58.7%), had no DR and mild NPDR was noted in 21 (6.7%) of the eyes. Moderate NPDR was detected in 66 (21.2%) of the eyes, severe NPDR in 1 (0.3%), and PDR in 41 (13.1%) composing a group of 34.6% of eyes with referable DR. The AI algorithms achieved a mean agreement of 79.4% for referable DR, but the results varied from 49.4% to 92.3%. The mean sensitivity for referable DR was 77.5% (95% CI 69.1–85.8) and specificity 80.6% (95% CI 72.1–89.2). The rate for images ungradable by AI varied from 0% to 28.2% (mean 1.9%). Nineteen out of 21 (90.5%) AI algorithms resulted in grading for DR at least in 98% of the images. Conclusions Fundus images captured with Optomed Aurora were suitable for DR screening. The performance of the AI algorithms varied considerably emphasizing the need for external validation of screening algorithms in real-world settings before their clinical application.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

Carbohydrate Ingestion before Exercise for Individuals with McArdle Disease: Survey Evidence of Implementation and Perception in Real-World Settings

In individuals with McArdle disease (IWMD), the ingestion of carbohydrates before exercise has previously been shown in laboratory studies to significantly decrease the exercising symptoms of the condition and increase exercise tolerance during the early stages of exercise. As a result, carbohydrate ingestion pre-exercise is currently included in management guidelines, and often advised by medical professionals treating the condition. The aim of the current study was to determine whether positive lab-based results for the ingestion of carbohydrate before exercise in laboratory studies are being effectively translated into practice and produce perceptions of the same positive outcomes in real-world settings (RWS). An online survey method was used to collect responses from 108 IWMD. Data collected on the amount and type of carbohydrate consumed prior to exercise found that most surveyed participants (69.6%) who supplied qualitative data (n = 45) consumed less than the 37 g currently recommended in management guidelines. Survey data also revealed a large variation in the type and amount of carbohydrate ingested when IWMDs are applying carbohydrate ingestion before exercise in RWS. Consistent with these findings, only 17.5% of participants stated that they found carbohydrate ingestion before exercise relieved or minimised their MD symptoms. Results suggest that positive lab-based findings (increased exercise tolerance) of carbohydrate ingestion before exercise are not being effectively translated to RWS for many IWMD. There is a need for improved patient education of IWMD on the application of carbohydrate ingestion before exercise in RWS.

An efficient deep learning model for tomato disease detection

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.

Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious Activities

Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This paper explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level (“None”, “mild”, “moderate”, and “severe”). Between-group analysis shows that discriminating ECG features (i.e., HR and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques. The findings need to be further scrutinized in real-world settings for the generalizability of the results.

Performance of the non-invasive point-of-care device, EzeCheck, for haemoglobin assessment in adults and children in community and institutional care settings.

Anaemia is a major public health problem, especially in resource constrained settings. Dependency on assessment of blood hemoglobin (Hgb) concentration impedes anemia detection, risk stratification and intervention. Thus, valid, frugal and scalable technologies are needed. EzeCheck is a noninvasive portable device developed in India for predicting hemoglobin levels in human beings aged 4 years and above using a finger-tip biosensor. In this assessment, we aimed to: (i) evaluate EzeCheck's performance with an automated whole blood hemato-analyzer (Sysmex XN 1000) as the gold standard, and (ii) estimate EzeCheck's agreement with Sahli's method and HemoCue (Hb-301) in real-world primary and higher care facilities. Paired assessments were done at five sites across India i.e., Bhubaneshwar, Shimla, Solan and Mashobra and Ulhasnagar. Participants across all age groups (4 years and above) were assessed. We used a range of statistical tests to evaluate the performance of EzeCheck. It was found that EzeCheck performed well across age and gender categories with convincing validity, concordance, precision and accuracy, and acceptable bias. While comparing EzeCheck with Hemato-analyzer, no statistically significant systematic bias was found. However, EzeCheck showed significant systematic bias when compared to Sahli's method and HemoCue. We concluded that EzeCheck could detect anemia (as per WHO Hgb cut-offs) in 'real-world settings' and 'across age and gender categories', with high sensitivity, specificity and accuracy, and can serve as a replacement to traditional methods of hemoglobin assessment. Further, for countries with higher prevalence of anemia where universal screening may be mandated, the positive predictive value of EzeCheck will be higher. The likelihood ratios also indicated that the device had moderate-to-good utility. EzeCheck is suitable for embedment into program and out-patient health care settings in resource constrained contexts as a spot-check hemoglobinometer.

Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions.

Digital approaches may be helpful in augmenting care to address unmet mental health needs, particularly for schizophrenia and severe mental illness (SMI). An international multidisciplinary group was convened to reach a consensus on the challenges and potential solutions regarding collecting data, delivering treatment, and the ethical challenges in digital mental health approaches for schizophrenia and SMI. The consensus development panel method was used, with an in-person meeting of 2 groups: the expert group and the panel. Membership was multidisciplinary including those with lived experience, with equal participation at all stages and coproduction of the consensus outputs and summary. Relevant literature was shared in advance of the meeting, and a systematic search of the recent literature on digital mental health interventions for schizophrenia and psychosis was completed to ensure that the panel was informed before the meeting with the expert group. Four broad areas of challenge and proposed solutions were identified: (1) user involvement for real coproduction; (2) new approaches to methodology in digital mental health, including agreed standards, data sharing, measuring harms, prevention strategies, and mechanistic research; (3) regulation and funding issues; and (4) implementation in real-world settings (including multidisciplinary collaboration, training, augmenting existing service provision, and social and population-focused approaches). Examples are provided with more detail on human-centered research design, lived experience perspectives, and biomedical ethics in digital mental health approaches for SMI. The group agreed by consensus on a number of recommendations: (1) a new and improved approach to digital mental health research (with agreed reporting standards, data sharing, and shared protocols), (2) equal emphasis on social and population research as well as biological and psychological approaches, (3) meaningful collaborations across varied disciplines that have previously not worked closely together, (4) increased focus on the business model and product with planning and new funding structures across the whole development pathway, (5) increased focus and reporting on ethical issues and potential harms, and (6) organizational changes to allow for true communication and coproduction with those with lived experience of SMI. This study approach, combining an international expert meeting with patient and public involvement and engagement throughout the process, consensus methodology, discussion, and publication, is a helpful way to identify directions for future research and clinical implementation in rapidly evolving areas and can be combined with measurements of real-world clinical impact over time. Similar initiatives will be helpful in other areas of digital mental health and similarly fast-evolving fields to focus research and organizational change and effect improved real-world clinical implementation.

Exposure-response relationship of guselkumab and the potential of serum proteomics in identifying predictive biomarker candidates in psoriasis.

Response to biologics in psoriasis varies in real-world settings. Serum biomarkers could aid biologic selection and dose modifications to improve patient outcomes while encouraging cost-effective care. To explore the exposure-response relationship for guselkumab (GUS), to define a GUS concentration target for optimal response and to evaluate the potential of serum protein levels as predictive biomarker candidates. This is a prospective, multicentric, cohort study in psoriasis patients treated with GUS. Serum GUS trough concentrations (TCs) collected at multiple timepoints were measured using an in-house immunoassay. Next, proximity extension assay technology (Target 96 Inflammation Panel Olink®) was used to measure serum protein levels in a subcohort including 38 GUS patients (week 0 and week 4), six psoriasis patients naive for systemic treatment and four healthy controls. Seventy-five patients participated and 400 samples were collected. Guselkumab TCs and clinical response were correlated at week 4, week 12 and in steady-state (≥20 weeks). Optimal responders (Psoriasis Area and Severity Index [PASI] ≤ 2) had significantly higher TCs than suboptimal responders from week 4 onwards in treatment. An optimal steady-state TC of 1.6 μg/mL was defined. Although TC and absolute PASI were lower and worse, respectively, in patients weighing ≥90 kg, clinical outcomes referred to desirable to excellent PASI ranges. Therefore, we do not recommend systematically higher GUS doses in obese patients. We could not reveal early differentially expressed proteins to distinguish future optimal from suboptimal responders. We demonstrated an exposure-response relationship for GUS and an optimal steady-state TC of 1.6 μg/mL in real-world psoriasis patients. Hereby, we deliver more evidence that therapeutic drug monitoring poses a promising strategy in optimizing GUS treatment. No biomarker candidates were identified through serum proteomics. We propose protein screening should be repeated in larger cohorts to continue the quest for predictive biomarkers.

Navigating work integrated learning (WIL) pathways for learners through creative media production: the Yagan Square digital tower WIL project

ABSTRACT Work-integrated learning (WIL) environments are designed to assist students in consolidating their acquired knowledge and skills, apply them to real-world settings and develop a professional identity. Promoting industry engagement and assisting students make links between what they are learning and how it applies to professional practice is one of our key roles as educators. Studies in the relationship between well-being and learning confirm the need for tertiary educators to consider how personal values, health and behaviours radically interact with skills development in the designing and implementation of our teaching and learning programs. It is an imperative we keep abreast of contemporary media practices, but also build effective networks with community and industry and generate partnerships to assist students as they navigate their way through the academy in an increasingly complex world. Informed by studio and project-based learning and experiential methodologies that facilitate student centred approaches with high levels of self-directed learning we discuss our respective pedagogical approaches to the Yagan Square digital tower project, Perth Australia, to promote student engagement with their own communities and how to work with an external organisation who also act as a client and mentors to students .

Common conditions of use elements. Atomic concepts for consistent and effective information governance

Myriad policy, ethical and legal considerations underpin the sharing of biological resources, implying the need for standardised and yet flexible ways to digitally represent diverse ‘use conditions’. We report a core lexicon of terms that are atomic, non-directional ‘concepts of use’, called Common Conditions of use Elements. This work engaged biobanks and registries relevant to the European Joint Programme for Rare Diseases and aimed to produce a lexicon that would have generalised utility. Seventy-six concepts were initially identified from diverse real-world settings, and via iterative rounds of deliberation and user-testing these were optimised and condensed down to 20 items. To validate utility, support software and training information was provided to biobanks and registries who were asked to create Sharing Policy Profiles. This succeeded and involved adding standardised directionality and scope annotations to the employed terms. The addition of free-text parameters was also explored. The approach is now being adopted by several real-world projects, enabling this standard to evolve progressively into a universal basis for representing and managing conditions of use.

Human versus Artificial Intelligence: Validation of a Deep Learning Model for Retinal Layer and Fluid Segmentation in Optical Coherence Tomography Images from Patients with Age-Related Macular Degeneration

Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.MethodsThis prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source “Pothos” air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.ResultsFollowing initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.ConclusionThis study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Effectiveness of dapagliflozin and sitagliptin fixed dose combination in indian patients with type 2 diabetes mellitus in real world settings: age stratified-analysis from a retrospective study

Effectiveness of dapagliflozin and sitagliptin fixed dose combination in indian patients with type 2 diabetes mellitus in real world settings: age stratified-analysis from a retrospective study

Assessing reliability of naïve respondent-driven sampling samples by using repeated surveys among people who inject drugs (PWID) in New Jersey

IntroductionRespondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings. MethodsWe assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance – People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses. ResultsThe study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples. ConclusionsIn conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

Advancing Implementation Practice: Supplemental Tools to Aid in Selection of Implementation Strategies and Outcomes for Healthcare Practitioners

AbstractThe field of implementation science has grown exponentially within healthcare and human service delivery settings. Scientists in the field have made tremendous strides in generating new knowledge aimed at increasing uptake of evidence into practice settings, including development of numerous theories, models, and frameworks to guide the process of implementation, strategies to operationalize optimal methods of implementing an evidence-based practice change, and designated outcomes to evaluate the progress of a change initiative. Despite these gains in scientific discovery surrounding implementation, routine use and application of this knowledge among healthcare practitioners seeking to implement a practice change in real world settings remains fragmented, risking development of a “secondary gap” between what we know from the science of implementation and what is actually done by frontline teams when implementing evidence-based practice changes. To address this gap, we developed two supplemental tools to bridge the language between implementation science and implementation practice: the Implementation Strategies Supplemental Tool (ISST), and the Implementation Outcomes Supplemental Tool (IOST). The purpose of this paper is to describe the development and intended use of these tools for healthcare practitioners seeking to use a science-informed approach when implementing an evidence-based practice change. We include a case example of how healthcare practitioners may apply these tools to improve implementation of evidence-based practices in real-world settings.

The association between weight loss medications and cardiovascular complications.

Obesity and its cardiovascular complications are major causes of morbidity and mortality. Little is known in real-world settings about the effect of newly approved antiobesity medications (AOMs) on cardiovascular complications among patients with obesity. This retrospective cohort study examined the association between newly approved AOM use and cardiovascular events among Medicare patients with obesity using data from 2020 to 2022. Patient age, gender, comorbidity scores, socioeconomic status, and baseline cardiovascular comorbidities were compared descriptively. Subgroup analysis compared variables by medication type. Relative risk and absolute risk of cardiovascular disease (CVD) events were estimated using Cox and Aalen regression models. The analysis included 5926 patients treated with semaglutide and tirzepatide, including Ozempic (5404 patients), Wegovy (375 patients), or Mounjaro (147 patients). Hypertension, type 2 diabetes, and hyperlipidemia were the most common comorbidities. For patients with AOMs, less incidence of heart failure (4.89% vs. 6.13%, p < 0.0001), atrial fibrillation (3.83% vs. 5.17%, p < 0.0001), arrhythmia (3.59% vs. 4.14%, p < 0.0153), and peripheral vascular disease (3.44% vs. 2.94%, p < 0.0395) was found versus patients without AOMs. Patients receiving AOMs showed an 8% risk reduction in any CVD. Protective effect on CVD was apparent over the first 375 days. Results suggest that utilization of AOMs effectively alleviates the high prevalence of CVD.

Generating real-world evidence in Alzheimer's disease: Considerations for establishing a core dataset.

Ongoing assessment of patients with Alzheimer's disease (AD) in postapproval studies is important for mapping disease progression and evaluating real-world treatment effectiveness and safety. However, interpreting outcomes in the real world is challenging owing to variation in data collected across centers and specialties and greater heterogeneity of patients compared with trial participants. Here, we share considerations for observational postapproval studies designed to collect harmonized longitudinal data from individuals with mild cognitive impairment or mild dementia stage of disease who receive therapies targeting the underlying pathological processes of AD in routine practice. This paper considers key study design parameters, including proposed aims and objectives, study populations, approaches to data collection, and measures of cognition, functional abilities, neuropsychiatric status, quality of life, health economics, safety, and drug utilization. Postapproval studies that capture these considerations will be important to provide standardized data on AD treatment effectiveness and safety in real-world settings.

Exploring the frontiers of therapeutic breadth of antifungal peptides: A new avenue in antifungal drugs.

The growing prevalence of fungal infections alongside rising resistance to antifungal drugs poses a significant challenge to public health safety. At the close of the 2000s, major pharmaceutical firms began to scale back on antimicrobial research due to repeated setbacks and diminished economic gains, leaving only smaller companies and research labs to pursue new antifungal solutions. Among various natural sources explored for novel antifungal compounds, antifungal peptides (AFPs) emerge as particularly promising. Despite their potential, AFPs receive less focus than their antibacterial counterparts. These peptides have been sourced extensively from nature, including plants, animals, insects, and especially bacteria and fungi. Furthermore, with advancements in recombinant biotechnology and computational biology, AFPs can also be synthesized in lab settings, facilitating peptide production. AFPs are noted for their wide-ranging efficacy, in vitro and in vivo safety, and ability to combat biofilms. They are distinguished by their high specificity, minimal toxicity to cells, and reduced likelihood of resistance development. This review aims to comprehensively cover AFPs, including their sources-both natural and synthetic-their antifungal and biofilm-fighting capabilities in laboratory and real-world settings, their action mechanisms, and the current status of AFP research.