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The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577–584. https://doi.org/10.2105/AJPH.2023.307225 )

Even after a decade of research on MoS2, it is still quite challenging to obtain high quality MoS2 films using a controlled synthesis technique. Out of all the available methods chemical vapor deposition (CVD) has proven to be a reliable technique to produce MoS2 thin films. The CVD growth process is sensitive to variety of parameters involved in the process. In the present work, we report the effect of pressure, temperature, gas flow, and position of substrate on the growth of MoS2 films. After several failures, the technique is optimized to synthesize high quality MoS2 films on quartz substrate. Further, the films are characterized using XRD, UV-Vis Spectroscopy, Photoluminescence Spectroscopy and Raman Spectroscopy. The main objective of this work is to formulate a method that produces MoS2 films using thermal CVD process by optimizing various parameters.

Interest in artificial intelligence (AI) has grown exponentially in recent years, attracting sensational headlines and speculation. While there is considerable potential for AI to augment clinical practice, there remain numerous practical implications that must be considered when exploring AI solutions. These range from ethical concerns about algorithmic bias to legislative concerns in an uncertain regulatory environment. In the absence of established protocols and examples of best practice, there is a growing need for clear guidance both for innovators and early adopters. Broadly, there are three stages to the innovation process: invention, development and implementation. In this paper, we present key considerations for innovators at each stage and offer suggestions along the AI development pipeline, from bench to bedside.

SummaryBackgroundSelf-harm is one of the most common presentations at accident and emergency departments in the UK and is a strong predictor of suicide risk. The UK Government has prioritised identifying risk factors and developing preventative strategies for self-harm. Machine learning offers a potential method to identify complex patterns with predictive value for the risk of self-harm.MethodsNational data in the UK Mental Health Services Data Set were isolated for patients aged 18‒30 years who started a mental health hospital admission between Aug 1, 2020 and Aug 1, 2021, and had been discharged by Jan 1, 2022. Data were obtained on age group, gender, ethnicity, employment status, marital status, accommodation status and source of admission to hospital and used to construct seven machine learning models that were used individually and as an ensemble to predict hospital stays that would be associated with a risk of self-harm.OutcomesThe training dataset included 23 808 items (including 1081 episodes of self-harm) and the testing dataset 5951 items (including 270 episodes of self-harm). The best performing algorithms were the random forest model (AUC-ROC 0.70, 95%CI:0.66-0.74) and the ensemble model (AUC-ROC 0.77 95%CI:0.75-0.79).InterpretationMachine learning algorithms could predict hospital stays with a high risk of self-harm based on readily available data that are routinely collected by health providers and recorded in the Mental Health Services Data Set. The findings should be validated externally with other real-world data.FundingThis study was supported by the Midlands and Lancashire Commissioning Support Unit.Research in contextEvidence before this studyDespite self-harm being repeatedly labelled as a national priority for psychiatric healthcare research, it remains challenging for clinicians to stratify the risk of self-harm in patients. National guidelines have highlighted deficiencies in care and attention is being paid towards the use of large datasets to develop evidence-based risk stratification strategies. However, many of the tools so far developed rely upon elements of the patient’s clinical history, which requires well curated datasets at a population level and previous engagement with care services at an individual level. Reliance upon elements of a patient’s clinical history also risks biasing against patients with missing data or against hospitals where data is poorly recorded.Added value of this studyIn this study, we use commissioning data that is routinely collected in the United Kingdom by healthcare providers with each hospital admission. Of the variables that were available for analysis, recursive feature elimination optimised our variable selection to include only age group, source of hospital admission, gender, and employment status. Machine learning algorithms were able to predict hospital episodes in which patients self-harmed in the majority of cases using a national dataset. Random forest and ensemble machine learning methods were the best-performing models. Sensitivity and specificity at predicting self-harm occurrence were 0.756 and 0.596, respectively, for the random forest model and 0.703 and 0.730 for the ensemble model. To our knowledge, this is the first study of its kind and represents an advance in the prediction of inpatient self-harm by limiting the amount of information required to make predictions to that which would be near-universally available at the point of the admission, nationally.Implications of all the available evidenceThere is a role for machine learning to be used to stratify the risk of self-harm when patients are admitted to mental health facilities, using only commissioning data that is easily accessible at the point of care. External validation of these findings is required as whilst the algorithms were tested on a large sample of national data, there remains a need for prospective studies to assess the real-world application of such machine learning models.

Artificial intelligence (AI) has been heralded as one of the key technological innovations of the 21st century. Within healthcare, much attention has been placed upon the ability of deductive AI systems to analyse large datasets to find patterns that would be unfeasible to program. Generative AI, including generative adversarial networks, are a newer type of machine learning that functions to create fake data after learning the properties of real data. Artificially generated patient data has the potential to revolutionise clinical research and protect patient privacy. Using novel techniques, it is increasingly possible to fully anonymise datasets to the point where no datapoint is traceable to any real individual. This can be used to expand and balance datasets as well as to replace the use of real patient data in certain contexts. This paper focuses upon three key uses of synthetic data: clinical research, data privacy and medical education. We also highlight ethical and practical concerns that require consideration.

Artificial intelligence (AI) is widely recognised as a transformative innovation and is already proving capable of outperforming human clinicians in the diagnosis of specific medical conditions, especially in image analysis within dermatology and radiology. These abilities are enhanced by the capacity of AI systems to learn from patient records, genomic information and real-time patient data. Uses of AI range from integrating with robotics to creating training material for clinicians. Whilst AI research is mounting, less attention has been paid to the practical implications on healthcare services and potential barriers to implementation. AI is recognised as a "Software as a Medical Device (SaMD)" and is increasingly becoming a topic of interest for regulators. Unless the introduction of AI is carefully considered and gradual, there are risks of automation bias, overdependence and long-term staffing problems. This is in addition to already well-documented generic risks associated with AI, such as data privacy, algorithmic biases and corrigibility. AI is able to potentiate innovations which preceded it, using Internet of Things, digitisation of patient records and genetic data as data sources. These synergies are important in both realising the potential of AI and utilising the potential of the data. As machine learning systems begin to cross-examine an array of databases, we must ensure that clinicians retain autonomy over the diagnostic process and understand the algorithmic processes generating diagnoses. This review uses established management literature to explore artificial intelligence as a digital healthcare innovation and highlight potential risks and opportunities.

The exposome is a concept that underlines the critical relationship between health and environmental exposures, including environmental toxicants. Currently, most environmental exposures that contribute to the exposome have not been characterized. Dried-blood spots (DBS) offer a cost-effective, reliable approach to characterize the blood exposome, which consists of diverse endogenous and exogenous chemicals, including persistent and bioaccumulating organic compounds. Current challenges involve prioritizing the identification by state-of-the-art mass spectrometry of likely up to tens of thousands of compounds present in blood; characterizing substances that represent a mixture of myriad constituent compounds; and detecting trace level contaminants, especially in quantity-limited matrices like DBS. This contribution reviews recent trends in DBS analysis of chemical pollutants and highlights the need for continued research in analytical chemistry to advance the field of exposomics.

Introduction During the course of the COVID-19 pandemic, there have been suggestions that various techniques could be employed to improve the fit and, therefore, the effectiveness of face masks. It is well recognized that improving fit tends to improve mask effectiveness, but whether these fit modifiers are reliable remains unexplored. In this study, we assess a range of common “fit hacks” to determine their ability to improve mask performance. Methods Between July and September 2020, qualitative fit testing was performed in an indoor living space. We used quantitative fit testing to assess the fit of both surgical masks and KN95 masks, with and without ‘fit hacks’, on four participants. Seven fit hacks were evaluated to assess impact on fit. Additionally, one participant applied each fit hack multiple times to assess how reliable hacks were when reapplied. A convenience of four participants took part in the study, three females and one male with a head circumference range of 54 to 60 centimetres. Results and discussion The use of pantyhose, tape, and rubber bands were effective for most participants. A pantyhose overlayer was observed to be the most effective hack. High degrees of variation were noted between participants. However, little variation was noted within participants, with hacks generally showing similar benefit each time they were applied on a single participant. An inspection of the fit hacks once applied showed that individual facial features may have a significant impact on fit, especially the nose bridge. Conclusions Fit hacks can be used to effectively improve the fit of surgical and KN95 masks, enhancing the protection provided to the wearer. However, many of the most effective hacks are very uncomfortable and unlikely to be tolerated for extended periods of time. The development of effective fit-improvement solutions remains a critical issue in need of further development.

Objective: The effectiveness of filtering facepiece respirators such as N95 respirators is heavily dependent on the fit. However, there have been limited efforts to discover the size of the gaps in the seal required to compromise filtering facepiece respirator performance, with prior studies estimating this size based on in vitro models. In this study, we measure the size of leak necessary to compromise the fit of N95 respirators. Methods: Two methods were used to create a gap of specific dimensions. A set of 3D-printed resin spacers and hollow steel rods were used to generate gaps in N95 respirators while worn on 2 participants. Occupational Safety and Health Administration (OSHA) quantitative fit testing methods were used to quantify mask performance with gaps between 0.4 and 2.9-mm diameters. Results: Gap size was regressed against fit factor, showing that overall, the minimum gap size to compromise N95 performance was between 1.5 mm2 and 3 mm2. Conclusions: These findings suggest the fit of a N95 respirator is compromised by gaps that may be difficult to visually detect. The study also adds to the body of evidence supporting the routine use of quantitative fit testing to ensure that masks are well-fitting.

Alcoholic fatty liver disease (AFLD) is characterized by lipid accumulation and inflammation and can progress to cirrhosis and cancer in the liver. AFLD diagnosis currently relies on histological analysis of liver biopsies. Early detection permits interventions that would prevent progression to cirrhosis or later stages of the disease. Herein, we have conducted the first comprehensive time-course study of lipids using novel state-of-the art lipidomics methods in plasma and liver in the early stages of a mouse model of AFLD, i.e., Lieber-DeCarli diet model. In ethanol-treated mice, changes in liver tissue included up-regulation of triglycerides (TGs) and oxidized TGs and down-regulation of phosphatidylcholine, lysophosphatidylcholine, and 20-22-carbon-containing lipid-mediator precursors. An increase in oxidized TGs preceded histological signs of early AFLD, i.e., steatosis, with these changes observed in both the liver and plasma. The major lipid classes dysregulated by ethanol play important roles in hepatic inflammation, steatosis, and oxidative damage. Conclusion: Alcohol consumption alters the liver lipidome before overt histological markers of early AFLD. This introduces the exciting possibility that specific lipids may serve as earlier biomarkers of AFLD than those currently being used.

Despite legal advances, India’s LGBTQ+ community still struggles to access healthcare without discrimination, reports Anmol Arora

There are two key emerging technologies which are anticipated to transform healthcare in the next 10 years: artificial intelligence (AI) and robotics. Of the two, AI has attracted particular interest due to scope for generativity and promising results from early studies into its potential implementation. Indeed, a recent study suggested that approximately 80% of pathologists believe that AI will become integrated in diagnostic workflows in the next decade.1 There is scope for both robotics and AI to transform pathology as a speciality in the near future, though the two innovations are not mutually exclusive. Simple robots which are narrowly programmed to perform some physical actions already exist, but to create advanced robots would require synergies with AI. Before we see robots take over the physical actions of pathologists, we are likely to see AI have significant impact in other ways. This can partly be attributed to Moravec’s paradox, an observation by AI researchers that, to program AI which is capable of advanced cognitive processes is often relatively straightforward compared with simple physical tasks.2 Pathology as a speciality is particularly pertinent to emerging AI research, which currently focusses on image and data analysis, two key elements of a pathologist’s role. Early research has begun to explore how AI may begin to affect pathology and improve patient care but the effects on pathology training remain relatively underexamined. Most research into AI has focussed on deductive systems. However, there are other types of AI which do not attract as much research or media speculation (table 1). Broadly speaking, there are four categories: View this table: Table 1 Comparison of different types of AI Deductive systems function by analysing data sets and finding patterns which would be infeasible for humans to program. Their uses are well …

Supercapacitors have gained a lot of attention due to their unique features like high power, long cycle life and environment-friendly nature. They act as a link for energy-power difference between a traditional capacitor (having high power) and fuel cells/batteries (having high energy storage). In this perspective, a worldwide research has been reported to address this and rapid progress has been achieved in the advancement of fundamental as well as the applied aspects of supercapacitors. Here, a concise description of technologies and working principles of different materials utilized for supercapacitors has been provided. The main focus has been on materials like carbon-based nanomaterials, metal oxides, conducting polymers and their nanocomposites along with some novel materials like metal-organic frameworks, MXenes, metal nitrides, covalent organic frameworks and black phosphorus. The performance of nanocomposites has been analysed by parameters like energy, capacitance, power, cyclic performance and rate capability. Some of the latest supercapacitors such as electrochromic supercapacitor, battery-supercapacitor hybrid device, electrochemical flow capacitor, alternating current line filtering capacitor, micro-supercapacitor, photo-supercapacitor, thermally chargeable supercapacitor, self-healing supercapacitor, piezoelectric and shape memory supercapacitor have also been discussed. This review covers the up-to-date progress achieved in novel materials for supercapacitor electrodes. The latest fabricated symmetric/asymmetric supercapacitors have also been reported.

Acknowledgements: We would like to thank Polygeia, a student-led global health think tank, for its support in organising the project. Specifically, we would like to thank Haowen Kwan (Polygeia) and Ananya Manchanda (University of Cambridge). We would also like to thank Elizabeth Evans (Costello Medical, UK) and Dr Simran Chana (NHS) for their advice during the project and for reviewing the manuscript., Healthcare as an industry is recognised as one of the most innovative. Despite heavy regulation, there is substantial scope for new technologies and care models to not only boost patient outcomes but to do so at reduced cost to healthcare systems and consumers. Promoting innovation within national health systems such as the National Health Service (NHS) in the United Kingdom (UK) has been set as a key target for health care professionals and policy makers. However, while the UK has a world-class biomedical research industry, several reports in the last twenty years have highlighted the difficulties faced by the NHS in encouraging and adopting innovations, with the journey from idea to implementation of health technology often taking years and being very expensive, with a high failure rate. This has led to the establishment of several innovation pathways within and around the NHS, to encourage the invention, development and implementation of cost-effective technologies that improve health care delivery. These pathways span local, regional and national health infrastructure. They operate at different stages of the innovation pipeline, with their scope and work defined by location, technology area or industry sector, based on the specific problem identified when they were set up. In this introductory review, we outline each of the major innovation pathways operating at local, regional and national levels across the NHS, including their history, governance, operating procedures and areas of expertise. The extent to which innovation pathways address current challenges faced by innovators is discussed, as well as areas for improvement and future study.

BackgroundSleep apnoea has a high disease burden but remains underdiagnosed, in part due to the expensive and resource intensive nature of polysomnography, its definitive investigation. Emerging literature suggests that it may be possible to detect sleep apnoea using single-lead ECG signals, such as those obtained from smartwatches. In this study, we use two forms of recurrent neural networks (RNNs) to detect sleep apnoea events from single-lead ECG signals.MethodsWe use single-lead ECG data from the PhysioNet Apnea-ECG database, which contains data from 70 patients. We train a bidirectional gated recurrent unit (GRU) model and a bidirectional long short-term memory (LSTM) model on labelled ECG signals from 35 patients and test the models on the remaining 35 patients in the dataset.ResultsBoth models achieved 97.1% accuracy, sensitivity and specificity to detect whether the ECG recordings belonged to a patient diagnosed with sleep apnoea. This corresponds to 34/35 patients in the dataset. At detecting individual apnoea events, the GRU and LSTM models achieved 90.4% and 91.7% accuracies respectively.DiscussionThe models achieved high levels of accuracy, specificity and sensitivity. Bidirectional RNNs are strengthened by the ability of the models to be informed by both past and future states when analysing sequential data, such as ECGs. The models also require minimal human intervention as they automatically extract features from the data. If single-lead ECGs prove a suitable tool for sleep apnoea detection, this may enhance the diagnosis of sleep apnoea and potentially allow widespread screening for the condition.ConclusionsWe note that using models such as bidirectional RNNs has the potential to augment model performance. However, more research and validation is required in order to test whether these may be applicable to other datasets and in clinical practice.

BackgroundThe coronavirus disease 2019 pandemic has led to global disruption of healthcare. Many students volunteered to provide clinical support. Volunteering to work in a clinical capacity was a unique medical education opportunity; however, it is unknown whether this was a positive learning experience or which volunteering roles were of most benefit to students.MethodsThe COVIDReady2 study is a national cross-sectional study of all medical students at medical schools in the United Kingdom. The primary outcome is to explore the experiences of medical students who volunteered during the pandemic in comparison to those who did not. We will compare responses to determine the educational benefit and issues they faced. In addition to quantitative analysis, thematic analysis will be used to identify themes in qualitative responses.DiscussionThere is a growing body of evidence to suggest that service roles have potential to enhance medical education; yet, there is a shortage of studies able to offer practical advice for how these roles may be incorporated in future medical education. We anticipate that this study will help to identify volunteer structures that have been beneficial for students, so that similar infrastructures can be used in the future, and help inform medical education in a non-pandemic setting.Trial registrationNot Applicable.

IntroductionThere is a growing body of evidence to support the wearing of face masks to reduce spread of infectious respiratory pathogens, including SARS-CoV-2. However, the literature exploring the effectiveness of homemade fabric face masks is still in its infancy. Developing an evidence base is an important step to ensure that public policy is evidence based and truly effective.MethodsTwo methodologies were used in this study: quantitative fit testing of various face masks to indicate their effectiveness and a survey of 710 US residents about their perceptions of face mask effectiveness. N95, surgical and two fabric face masks were tested on an individual twenty five times each using a TSI 8038+ machine. Our survey was distributed by Qualtrics XM, asking participants to estimate the effectiveness of N95, surgical and fabric face masks.Results and DiscussionOur results indicate that fabric face masks blocked between 62.6% and 87.1% of fine particles, whereas surgical masks protected against an average of 78.2% of fine particles. N95 masks blocked 99.6% of fine particles. Survey respondents tended to underestimate the effectiveness of masks, especially fabric masks. Together these results suggest that fabric masks may be a useful tool in the battle against the COVID-19 pandemic and that increasing public awareness of the effectiveness of fabric masks may help in this endeavour.

Introduction The COVID-19 pandemic has made well-fitting face masks a critical piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks has been acknowledged, there remains a lack of understanding about the role of good fit in rendering protective equipment useful. In addition, supply chain constraints have caused some organizations to abandon traditional quantitative or/and qualitative fit testing, and instead, have implemented subjective fit checking. Our study seeks to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit checks by comparing fit check results to quantitative fit testing results. Methods Seven participants first evaluated N95 and KN95 respirators by performing a fit check. Participants then underwent quantitative fit testing wearing five N95 respirators, a KN95 respirator, a surgical mask, and fabric masks. Results N95 respirators offered higher degrees of protection than the other categories of masks tested; however, it should be noted that most N95 respirators failed to fit the participants adequately. Fit check responses had poor correlation with quantitative fit factor scores. KN95, surgical, and fabric masks achieved low fit factor scores, with little protective difference recorded between respiratory protection options. In addition, small facial differences were observed to have a significant impact on quantitative fit. Conclusion Fit is critical to the level of protection offered by respirators. For an N95 respirator to provide the promised protection, it must fit the participant. Performing a fit check via NHS self-assessment guidelines was an unreliable way of determining fit.

ObjectiveWith much of the public around the world depending on fabric face masks to protect themselves and others, it is essential to understand how the protective ability of fabric masks can be enhanced. This study evaluated the protection offered by eighteen fabric masks designs. In addition, it assessed the benefit of including three design features: insert filters, surgical mask underlayers, and nose wires.MethodsQuantitative fit tests were conducted on different masks and with some additional design features. An array of fabric masks were tested on a single participant to account for variability in face shapes. The effects of insert filters, surgical mask underlayers and nose wires were also assessed.ResultsAs expected, the fabric masks offered low degrees of protection; however, alterations in design showed significant increase in their protective ability. The most effective designs were multi-layered masks that fit tightly to the face and lacked dead space between the user and mask. Also, low air-resistance insert filters and surgical mask underlays provided the greatest increase in protection.ConclusionsOur findings indicate substantial heterogeneity in the protection offered by various fabric face masks. We also note some design features which may enhance the protection these masks offer.

Additional file 1. Appenfix B. Survey. *Compulsory (only three questions).

Background Despite a growing body of evidence suggesting that short sleep duration may be linked to adverse metabolic outcomes, how these associations differ between age groups remains unclear. We use eight years of data from the UK National Diet and Nutritional Survey (NDNS) (2008–2016) to analyse cross-sectional relationships between sleep duration and metabolic risk in participants aged 11–70 years. Methods Participants (n = 2008) who provided both metabolic risk and sleep duration data were included. Self-reported sleep duration was standardised by age, to account for differences in age-related sleep requirements. A standardised metabolic risk score was constructed, comprising: waist circumference, blood pressure, serum triglycerides, serum high-density lipoprotein cholesterol, and fasting plasma glucose. Regression models were constructed across four age groups from adolescents to older adults. Results Overall, decreased sleep duration (hrs) was associated with an increased metabolic risk (standard deviations) with significant quadratic (B:0.028 [95%CI: 0.007, 0.050]) and linear (B:-0.061 [95%CI: -0.111, -0.011]) sleep duration coefficients. When separated by age group, stronger associations were seen among mid-aged adults (36-50y) (quadratic coefficient: 0.038 [95%CI: 0.002, 0.074]) compared to other age groups (e.g. adolescents (11-18y), quadratic coefficient: -0.009 [95%CI: -0.042, 0.025]). An increased difference between weekend and weekday sleep was only associated with increased metabolic risk in adults aged 51–70 years (B:0.18 [95%CI: 0.005, 0.348]). Conclusions Our results indicate that sleep duration is linked to adverse metabolic risk and suggest heterogeneity between age groups. Longitudinal studies with larger sample sizes are required to explore long-term effects of abnormal sleep and potential remedial benefits.

Objective:Qualitative fit testing is a popular method of ensuring the fit of sealing face masks such as N95 and FFP3 masks. Increased demand due to the coronavirus disease 2019 (COVID-19) pandemic has led to shortages in testing equipment and has forced many institutions to abandon fit testing. Three key materials are required for qualitative fit testing: the test solution, nebulizer, and testing hood. Accessible alternatives to the testing solution have been studied. This exploratory qualitative study evaluates alternatives to the nebulizer and hoods for performing qualitative fit testing.Methods:Four devices were trialed to replace the test kit nebulizer. Two enclosures were tested for their ability to replace the test hood. Three researchers evaluated promising replacements under multiple mask fit conditions to assess functionality and accuracy.Results:The aroma diffuser and smaller enclosures allowed participants to perform qualitative fit tests quickly and with high accuracy.Conclusions:Aroma diffusers show significant promise in their ability to allow individuals to quickly, easily, and inexpensively perform qualitative fit testing. Our findings indicate that aroma diffusers and homemade testing hoods may allow for qualitative fit testing when conventional apparatus is unavailable. Additional research is needed to evaluate the safety and reliability of these devices.