Citations

BACKGROUND RelabotulinumtoxinA (RelaBoNT-A) is a complex-free, ready-to-use, liquid botulinum toxin A. OBJECTIVE Efficacy/safety of RelaBoNT-A treatment for lateral canthal lines (LCL). METHODS Randomized adults received RelaBoNT-A (30 U/side; n = 230) or placebo (n = 73) during a 6-month, double-blind, Ph3 study (Relabotulinumtoxin Aesthetic Development Study-2 [READY-2]). Primary end points (Month 1, maximum smile) comprised: composite ≥2-grade responder rate using concurrent LCL severity investigator live assessment (LCL-ILA) and subject live assessment (LCL-SLA); LCL-ILA 0 (none)/1 (mild) responder rate. Subject satisfaction and adverse events were also reported. RESULTS Month 1 composite ≥2-grade responder rates were 51.8% (RelaBoNT-A) and 1.4% (placebo; (p < .001). Month 1 none/mild LCL-ILA responder rates were 87.2% (RelaBoNT-A) and 11.9% (placebo; p < .001). Onset was reported Day 1 by 34%. At Month 6, LCL-ILA responder rates for RelaBoNT-A remained at 23.3% (none/mild) and 35.9% (≥1-grade improvement). Median return to baseline severity was 24.7 weeks; 64% (RelaBoNT-A group) had not returned to baseline at Month 6. RelaBoNT-A satisfaction was high through Month 6 (71%). Mild/moderate treatment-related adverse events occurred in 6.1% (RelaBoNT-A) and 5.5% (placebo). CONCLUSION RelaBoNT-A (60 U) treatment provided statistically significant improvement of moderate-to-severe LCL. One-third of subjects reported onset within 1 day and improvements were maintained through Month 6. Treatment satisfaction was high. RelaBoNT-A was well tolerated.

Background Identifying eloquent regions associated with poor outcomes based on CT perfusion (CTP) may help inform personalized decisions on selection for endovascular therapy (EVT) in patients with large vessel occlusion (LVO) ischemic stroke. This study aimed to characterize the relationship between CTP-defined hypoperfusion and National Institutes of Health Stroke Scale (NIHSS) subitem deficits. Methods Patients with anterior circulation LVO, baseline CTP, itemized NIHSS at presentation and 24 hours were included. CTP was analyzed using e-CTP (Brainomix, UK). Time to maximal contrast (Tmax) prolongation was defined as >6 s, and penumbra as the difference between Tmax and ischemic core (relative cerebral blood flow<30%). Voxel-lesion-symptom mapping was performed using sparse canonical correlation analysis. For each NIHSS subitem, and total NIHSS, the associations were plotted between Tmax voxels with baseline NIHSS, and penumbra voxels with delta NIHSS (24 hours minus baseline). Results This study included 171 patients. Total NIHSS was predicted by hypoperfusion in left frontal cortex and subcortical white matter tracts. Voxels associated with neurological recovery were symmetrical and subcortical. Limb deficits were associated with respective motor cortex regions and descending motor tracts, with negative correlation within the contralateral hemispheres. A similar but smaller cluster of voxels within the penumbra was associated with NIHSS improvement. Language impairment correlated with left frontal cortex and superior temporal gyrus voxels. With the exception of dysarthria, significant associations were observed and more diffusely distributed in all other NIHSS subitems. Conclusions These results demonstrate the feasibility of hypoperfusion-to-symptom mapping in LVO. Symptom-based mapping from presenting imaging could refine treatment decisions targeting specific neurological deficits.

Background As the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms. Objective To provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms. Methods This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened. The study included original research articles that evaluated the predictive abilities of various machine learning algorithms for determining the success of flow diverters in achieving aneurysm occlusion. Results Five studies out of 217 were included based on our criteria. The included studies used various variables (patient demographics, aneurysm and parent artery characteristics, flow diverter and hemodynamic-related features, and angiographic parametric imaging) to predict flow diverter treatment outcomes. The machine learning algorithms used, along with their respective accuracy rates, were as follows: logistic regression (61% and 85%), support vector machine (88%), Gaussian support vector machine (90%), linear support vector machine (85%), decision tree (80%), random forest (87%), k-nearest neighbors (83% and 85%), XGBoost (87%), CatBoost (86%), deep neural networks (77.9%), and recurrent neural networks (74%).Two studies trained the machine learning models with both all features and the most significant features. Both studies observed that the accuracy of machine learning models decreased by removing the insignificant features. Conclusion The current literature indicates that machine learning algorithms can be trained to predict the success of flow diverters with an accuracy of up to 90%.

Background Medical oaths and ethical codes play a crucial role in guiding physicians through their professional responsibilities. This study extends prior research on ethical codes created by students at the Medical School for International Health (MSIH) by analysing codes from the years 2007 to 2022. Objective The objective of this study is to evaluate changes in the ethical principles emphasised by MSIH students over time and to compare these with previous cohorts. Methods Ethical codes from MSIH classes (2007–2022) were analysed using the framework developed by Baker. This framework categorises oaths into four sections: preamble, covenant, code of duties to patients and peroration. The focus was primarily on the code of duties to patients. Quantitative analysis was used to assess the prevalence of specific ethical principles. Results The study found an increased emphasis on principles such as ‘respect for patients’ and ‘cultural competence/sensitivity’, while traditional values like ‘benevolence’ and ‘honesty’ were mentioned less frequently. New themes, including ‘patient autonomy’ and ‘cultural competence’, emerged, reflecting a shift towards patient-centred care and justice, though emerging issues like artificial intelligence ethics were not addressed. Conclusions MSIH students’ ethical codes have evolved to emphasise patient respect and cultural competence. The findings suggest a maturation in the ethical perspectives of medical students, though gaps remain in addressing contemporary challenges. Future studies should explore how ethical codes can incorporate emerging issues in healthcare.