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In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

Effective, scalable dementia prevention interventions are needed to address modifiable risk factors given global burden of dementia and challenges in developing disease-modifying treatments. A single-blind randomized controlled trial assessed an online multidomain lifestyle intervention to prevent cognitive decline over 3 years. Participants were dementia-free community-dwelling Australians aged 55–77 years with modifiable dementia risk factors. Eligible participants ( n  = 6,104, 64% female) were randomized 1:1 to a personalized schedule of online coaching in two to four modules (targeting physical activity, nutrition, cognitive activity and depression or anxiety) or a control group that received module-eligible information only. At 3 years, the mean change in a global cognitive composite, the primary outcome, was met. The mean changes in z scores were 0.28 (95% confidence interval (CI): 0.25–0.32) for intervention, 0.10 (95% CI: 0.07–0.13) for control and 0.18 (95% CI: 0.13–0.23, P  < 0.001) for the between-group difference. Trial-related adverse events occurred in 19 (0.60%) intervention and 1 (0.03%) control participant. Randomization of this internet-delivered lifestyle intervention tailored to individual dementia risk factors resulted in significantly better cognition in older adults over 3 years. This intervention is scalable with the potential for population-level rollout that may delay cognitive decline in the general community. Australian New Zealand ClinicalTrials.gov registration: ACTRN12618000851268. An online tailored coaching intervention, delivered over 3 years, improved global cognition, dementia risk, physical activity, nutrition and depression in older dementia-free community-dwelling individuals.

Background: Identification of prognostic biomarkers in cancers is a crucial step to improve overall survival (OS). Although mutations in tumour protein 53 (TP53) is prevalent in astrocytoma, the prognostic effects of TP53 mutation are unclear. Methods: In this retrospective study, we sequenced TP53 exons 1 to 10 in a cohort of 102 lower-grade glioma (LGG) subtypes and determined the prognostic effects of TP53 mutation in astrocytoma and oligodendroglioma. Publicly available datasets were analysed to confirm the findings. Results: In astrocytoma, mutations in TP53 codon 273 were associated with a significantly increased OS compared to the TP53 wild-type (HR (95% CI): 0.169 (0.036–0.766), p = 0.021). Public datasets confirmed these findings. TP53 codon 273 mutant astrocytomas were significantly more chemosensitive than TP53 wild-type astrocytomas (HR (95% CI): 0.344 (0.13–0.88), p = 0.0148). Post-chemotherapy, a significant correlation between TP53 and YAP1 mRNA was found (p = 0.01). In O (6)-methylguanine methyltransferase (MGMT) unmethylated chemotherapy-treated astrocytoma, both TP53 codon 273 and YAP1 mRNA were significant prognostic markers. In oligodendroglioma, TP53 mutations were associated with significantly decreased OS. Conclusions: Based on these findings, we propose that certain TP53 mutant astrocytomas are chemosensitive through the involvement of YAP1, and we outline a potential mechanism. Thus, TP53 mutations may be key drivers of astrocytoma therapeutic efficacy and influence survival outcomes.

This study aimed to examine psychometric properties of the Adherence to Refills and Medications Scale (ARMS) in people with gout. We conducted exploratory factor analysis (EFA) and tested internal consistency (ordinal and Cronbach’s alpha coefficients) and agreement (intraclass correlation coefficient (2,1)) in ARMS scores across three timepoints (baseline, 6, and 12 months) in 487 people with gout. The Kruskal–Wallis test, Spearman’s rank, Kendall’s tau-b correlations, and logistic regression were used to examine the criterion-related validity of the ARMS and factors associated with the ARMS. EFA suggested a one-factor structure, explaining 43.2% of total variance. High internal consistency (ordinal alpha = 0.902 at baseline) and moderate agreement in ARMS scores over time (ICCs > 0.5; p < 0.001) were observed. Lower ARMS scores (indicating better adherence) predicted achieving target serum urate (OR, 0.89; 95% CI, 0.83–0.95; p < 0.001), but not urate-lowering therapy (ULT) adherence (Proportion of Days Covered (PDC) ≥ 80%) (OR, 0.93; 95% CI, 0.81–1.05; p = 0.261). Negative correlations between ARMS and PDC were not statistically significant (Kendall’s tau-b, r =  − 0.126, p = 0.078; Spearman’s rho =  − 0.173, p < 0.073). Differences in median ARMS scores (IQR) of 16 (14–20), 13 (12–15), and 17.5 (15–21) in three groups of participants who reported (1) not taking ULT, (2) taking ULT and adherent, and (3) taking ULT but not adherent, respectively, were statistically significant (p < 0.001). Age was the only patient factor independently associated with optimal adherence (ARMS score = 12) (OR, 1.91; 95% CI, 1.50–2.43; p < 0.001). The ARMS is a reliable and valid measure of medication adherence behaviours in people with gout, justifying its use in gout medication adherence research.Key Points• Valid, practical, and efficient methods of measuring adherence to medications are needed in people with gout.• Commonly used medication adherence questionnaires have limited validity or have not been validated in people with gout.• The Adherence to Refills and Medications Scale (ARMS) has been proven valid and practical in many chronic illnesses but has not been validated in people with gout.• We showed the ARMS is valid and reliable for use in people with gout.

Background Medical imaging related knowledge and skills are widely used in clinical practice. However, radiology teaching methods and resultant knowledge among medical students and junior doctors is variable. A systematic review and meta-analysis was performed to compare the impact of different components of radiology teaching methods (active versus passive teaching, eLearning versus traditional face-to-face teaching) on radiology knowledge / skills of medical students. Methods PubMed and Scopus databases were searched for articles published in English over a 15-year period ending in June 2021 quantitatively comparing the effectiveness of undergraduate medical radiology education programs regarding acquisition of knowledge and/or skills. Study quality was appraised by the Medical Education Research Study Quality Instrument (MERSQI) scoring and analyses performed to assess for risk of bias. A random effects meta-analysis was performed to pool weighted effect sizes across studies and I 2 statistics quantified heterogeneity. A meta-regression analysis was performed to assess for sources of heterogeneity. Results From 3,052 articles, 40 articles involving 6,242 medical students met inclusion criteria. Median MERSQI score of the included articles was 13 out of 18 possible with moderate degree of heterogeneity (I 2  = 93.42%). Thematic analysis suggests trends toward synergisms between radiology and anatomy teaching, active learning producing superior knowledge gains compared with passive learning and eLearning producing equivalent learning gains to face-to-face teaching. No significant differences were detected in the effectiveness of methods of radiology education. However, when considered with the thematic analysis, eLearning is at least equivalent to traditional face-to-face teaching and could be synergistic. Conclusions Studies of educational interventions are inherently heterogeneous and contextual, typically tailored to specific groups of students. Thus, we could not draw definitive conclusion about effectiveness of the various radiology education interventions based on the currently available data. Better standardisation in the design and implementation of radiology educational interventions and design of radiology education research are needed to understand aspects of educational design and delivery that are optimal for learning. Trial registration Prospero registration number CRD42022298607.

Digital health interventions have potential to improve outcomes in high risk cardiac patients through remote monitoring and patient education but introduce accessibility issues among patients who lack suitable smartphones. We will evaluate the effectiveness and scalability of the TeleClinical Care Cardiac (TCCCardiac) platform, that aims to reduce hospital readmissions and improve adherence to care. A pragmatic, all-comers trial with nested randomization, where patients being discharged home following an admission with acute myocardial infarction (MI) or decompensated heart failure (HF) are divided into 3 cohorts pragmatically, based on their access to technology. Cohort 1 participants are randomized to either the TCCCardiac model of care or usual care alone. The intervention includes a smartphone app, blood pressure monitor, weight scales, and a pulse oximeter, with remote monitoring of daily inputs by clinicians. Cohort 2 participants, with incompatible mobile phones, are randomized to receive educational content by SMS (TCC-Text) or usual care alone. Cohort 3 participants with no mobile phone receive usual care alone. The primary objective is to compare six-month readmission rates (primary end point) in Cohort 1. Secondary objectives include comparing the primary end point, evaluating the cost-effectiveness and overall impact across all cohorts and interventions, and process evaluation to understand the reach, adoption, and effectiveness of the full intervention. Follow-up includes 6-month interview for Cohort 1 and data linkage for all cohorts for 12-month outcomes. The trial began in July 2021. Recruitment was slower than expected due to delays and interruptions related to COVID-19 and the final enrolment date was set for October 2023, by which time 873 participants had been enrolled: 553 in Cohort 1 (63.3%), 161 in Cohort 2 (18.4%), and 159 in Cohort 3 (18.2%). Data linkage is anticipated in May 2025, which includes a 6-month delay to ensure 12-month data will be available for all study patients, followed by the analysis of results. TCCCardiac is the first large-scale study to assess smartphone-based messaging and remote monitoring in high-risk cardiac patients post-hospitalization. The study's pragmatic design and process evaluation aim to enhance future implementation. Australian New Zealand Clinical Trials Registry Number ACTRN12621000754842.

IntroductionMyopia is the most common refractive error worldwide, but each dioptre increase in myopia leads to an increased risk of degenerative eye disease and permanent vision impairment. Soft contact lens (CL) designs have been developed to slow myopia and potentially reduce long-term risk, but there is still a need for additional designs of varied materials and parameters to cater for diverse patient needs. The MultifocAL COntact Lenses for Myopia control study aims to compare the efficacy of the Acuvue Oasys for Presbyopia (AOP) CL against the Food and Drug Administration approved MiSight 1-Day multifocal CL in controlling progressive myopia in children using a non-inferiority contralateral eye design.Methods and analysisA double-blind, contralateral eye, non-inferiority, randomised, controlled clinical trial will be conducted at University of New South Wales Sydney, Australia (UNSW). Children (6 to 12 years of age, inclusive) will be randomised to wear AOP in their right or left eye, with the MiSight 1-Day CL fitted to the contralateral eye. The primary outcome is the difference in axial length and cycloplegic objective refraction change between the two CLs over 12 months. Additional outcomes include quality of life, pupillometry and adherence to treatment. To achieve a statistical power of 80% to demonstrate non-inferiority of the AOP to the MiSight 1-Day and taking into consideration a 20% discontinuation rate, the calculated sample size is 72. This trial started recruitment during the recent COVID-19 pandemic in January 2021.Ethics and disseminationEthics approval has been obtained from the UNSW Human Research Ethics Committee (HC200052), and the study complies with the Declaration of Helsinki and the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Good Clinical Practice guidelines. The results of this trial will be disseminated in peer-reviewed publications and conference presentations.Trial registration numberACTRN12620000159954, CTN-00 282–1 v2, NCT06887920.

Background Parent–Child Interaction Therapy—Toddler (PCIT-T) is an attachment-informed intervention model designed to meet the specific developmental needs of toddlers aged 12–24 months presenting with challenging behaviors. Methods This study used a randomized controlled design to evaluate outcomes of PCIT-T for children aged 14–24 months with disruptive behaviors. Ninety toddlers with parent-reported disruptive behavior were randomly allocated to PCIT-T (intervention), an active control condition (Circle of Security– Parenting™; COS-P), or a non-treatment control condition (wait-list; WL). Outcomes were assessed at baseline (Time 1), post treatment/post waitlist (Time 2) and 4-month follow-up (Time 3). Results At follow-up, the PCIT-T group displayed the highest levels of parenting sensitivity and positive parental verbalizations, and the lowest levels of negative child-directed verbalizations and non-attuned mind-minded statements. Of the three groups, the PCIT-T group showed the greatest degree of change on these variables, followed by the COS-P group and then the non-treated controls. The PCIT-T group were also the only group to show significant within-group improvements in sensitivity, self-reported parental reflectiveness, empathy and emotional understanding, parent-reported child social competence, child internalizing problems, and general behavior issues. Significant reductions in parental stress, child externalizing behaviors and parenting behaviors were seen for both the PCIT-T and COS-P groups. Conclusions Delivered in the early intervention period of toddlerhood, Parent–Child Interaction Therapy—Toddler has the potential to bring about significant changes for children presenting with early onset behavioral issues. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR), 12,618,001,554,257. Registered 24 September 2018 – retrospectively registered, https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618001554257 .

Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945.

Background The COVID-19 pandemic and associated social distancing regulations have led to an increased risk of social isolation and physical inactivity, particularly among older adults. The benefits of physical activity for reducing fall risk and improving mood and mental functioning have been well documented. The aim of this trial is to investigate the effect of the MovingTogether programme on psychological distress (primary outcome) and physical activity, social capital, cognition, concern about falling, loneliness, physical functioning, quality of life and physical activity enjoyment (secondary outcomes). Methods A randomised controlled trial with a waitlist control will be conducted, recruiting 80 adults aged 60+ years with access to Facebook and a computer or tablet and not currently meeting the aerobic physical activity guidelines. Randomisation will be completed using REDCap. The intervention group ( n = 40) will join a private Facebook group where allied health facilitators will provide targeted healthy lifestyle education throughout the 10-week programme with weekly telehealth group calls. Intervention participants will also be provided access to tailored strength and aerobic exercise guidance and an evidence-based eHealth balance exercise programme. Psychological distress and secondary outcomes will be assessed at baseline, 11 weeks (post-intervention) and 16 weeks (1-month follow-up). Linear mixed models will be applied for each outcome measure as per an intention-to-treat approach to determine the between-group differences. Secondary analyses are planned in people with greater adherence and those with higher psychological distress. Discussion COVID-19 has highlighted the need for scalable, effective and novel methods to improve and protect the health of older adults. The integration of an evidence-based fall prevention programme with a mental health-informed online health promotion programme may help to improve mental and physical health outcomes among older adults. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621001322820p. Registered on 29 September 2021