Twin-Free GaAs Nanosheets by Selective Area Growth: Implications for Defect-Free Nanostructures

Highly perfect, twin-free GaAs nanosheets grown on (111)B surfaces by selective area growth (SAG) are demonstrated. In contrast to GaAs nanowires grown by (SAG) in which rotational twins and stacking faults are almost universally observed, twin formation is either suppressed or eliminated within properly oriented nanosheets are grown under a range of growth conditions. A morphology transition in the nanosheets due to twinning results in surface energy reduction, which may also explain the high twin-defect density that occurs within some III–V semiconductor nanostructures, such as GaAs nanowires. Calculations suggest that the surface energy is significantly reduced by the formation of {111}-plane bounded tetrahedra after the morphology transition of nanowire structures. By contrast, owing to the formation of two vertical {11̅0} planes which comprise the majority of the total surface energy of nanosheet structures, the energy reduction effect due to the morphology transition is not as dramatic as that for nanowire structures. Furthermore, the surface energy reduction effect is mitigated in longer nanosheets which, in turn, suppresses twinning

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Deep learning–based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling “hands-on” education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations

Proposal of a new slit-lamp shield for ophthalmic examination and assessment of its effectiveness using computational simulations

PURPOSE: This study aimed to use computational models for simulating the movement of respiratory droplets when assessing the efficacy of standard slit-lamp shield versus a new shield designed for increased clinician comfort as well as adequate protection. METHODS: Simulations were performed using the commercial software Star-CCM+. Respiratory droplets were assumed to be 100% water in volume fraction with particle diameter distribution represented by a geometric mean of 74.4 (±1.5 standard deviation) μm over a 4-min duration. The total mass of respiratory droplets expelled from patients' mouths and droplet accumulation on the manikin were measured under the following three conditions: with no slit-lamp shield, using the standard slit-lamp shield, and using our new proposed shield. RESULTS: The total accumulated water droplet mass (kilogram) and percentage of expelled mass accumulated on the shield under the three aforementioned conditions were as follows: 5.84e-10 kg (28% of the total weight of particle emitted that settled on the manikin), 9.14e-13 kg (0.045%), and 3.19e-13 (0.015%), respectively. The standard shield could shield off 99.83% of the particles that would otherwise be deposited on the manikin, which is comparable to 99.95% for the proposed design. Conclusion: Slit-lamp shields are effective infection control tools against respiratory droplets. The proposed shield showed comparable effectiveness compared with conventional slit-lamp shields, but with potentially enhanced ergonomics for ophthalmologists during slit-lamp examinations

Large language models approach expert-level clinical knowledge and reasoning in ophthalmology:A head-to-head cross-sectional study

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64–90%), ophthalmology trainees (median 59%, range 57–63%), and unspecialised junior doctors (median 43%, range 41–44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p > 0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p < 0.05). LLMs are approaching expert-level knowledge and reasoning skills in ophthalmology. In view of the comparable or superior performance to trainee-grade ophthalmologists and unspecialised junior doctors, state-of-the-art LLMs such as GPT-4 may provide useful medical advice and assistance where access to expert ophthalmologists is limited. Clinical benchmarks provide useful assays of LLM capabilities in healthcare before clinical trials can be designed and conducted

Towards clinical AI fairness: A translational perspective

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations and discusses potential innovations. We searched five databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from three primary perspectives, including data quality, modeling strategies, and FL frameworks. Out of the 1160 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research

Association between digital smart device use and myopia: a systematic review and meta-analysis

Background- Excessive use of digital smart devices, including smartphones and tablet computers, could be a risk factor for myopia. We aimed to review the literature on the association between digital smart device use and myopia. Methods- In this systematic review and meta-analysis we searched MEDLINE and Embase, and manually searched reference lists for primary research articles investigating smart device (ie, smartphones and tablets) exposure and myopia in children and young adults (aged 3 months to 33 years) from database inception to June 2 (MEDLINE) and June 3 (Embase), 2020. We included studies that investigated myopia-related outcomes of prevalent or incident myopia, myopia progression rate, axial length, or spherical equivalent. Studies were excluded if they were reviews or case reports, did not investigate myopia-related outcomes, or did not investigate risk factors for myopia. Bias was assessed with the Joanna Briggs Institute Critical Appraisal Checklists for analytical cross-sectional and cohort studies. We categorised studies as follows: category one studies investigated smart device use independently; category two studies investigated smart device use in combination with computer use; and category three studies investigated smart device use with other near-vision tasks that were not screen-based. We extracted unadjusted and adjusted odds ratios (ORs), β coefficients, prevalence ratios, Spearman's correlation coefficients, and p values for associations between screen time and incident or prevalent myopia. We did a meta-analysis of the association between screen time and prevalent or incident myopia for category one articles alone and for category one and two articles combined. Random-effects models were used when study heterogeneity was high (I2>50%) and fixed-effects models were used when heterogeneity was low (I2≤50%). Findings- 3325 articles were identified, of which 33 were included in the systematic review and 11 were included in the meta-analysis. Four (40%) of ten category one articles, eight (80%) of ten category two articles, and all 13 category three articles used objective measures to identify myopia (refraction), whereas the remaining studies used questionnaires to identify myopia. Screen exposure was measured by use of questionnaires in all studies, with one also measuring device-recorded network data consumption. Associations between screen exposure and prevalent or incident myopia, an increased myopic spherical equivalent, and longer axial length were reported in five (50%) category one and six (60%) category two articles. Smart device screen time alone (OR 1·26 [95% CI 1·00–1·60]; I2=77%) or in combination with computer use (1·77 [1·28–2·45]; I2=87%) was significantly associated with myopia. The most common sources of risk of bias were that all 33 studies did not include reliable measures of screen time, seven (21%) did not objectively measure myopia, and nine (27%) did not identify or adjust for confounders in the analysis. The high heterogeneity between studies included in the meta-analysis resulted from variability in sample size (range 155–19 934 participants), the mean age of participants (3–16 years), the standard error of the estimated odds of prevalent or incident myopia (0·02–2·21), and the use of continuous (six [55%] of 11) versus categorical (five [46%]) screen time variables Interpretation- Smart device exposure might be associated with an increased risk of myopia. Research with objective measures of screen time and myopia-related outcomes that investigates smart device exposure as an independent risk factor is required

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London