Patient and Clinician Perceptions of the Pulse Oximeter in a Remote Monitoring Setting for COVID-19: Qualitative Study.

BACKGROUND: As a response to the COVID-19 pandemic, the Sydney Local Health District in New South Wales, Australia, launched the rpavirtual program, the first full-scale virtual hospital in Australia, to remotely monitor and follow up stable patients with COVID-19. As part of the intervention, a pulse oximeter wearable device was delivered to patients to monitor their oxygen saturation levels, a critical indicator of COVID-19 patient deterioration. Understanding users' perceptions toward the device is fundamental to assessing its usability and acceptability and contributing to the effectiveness of the intervention, but no research to date has explored the user experience of the pulse oximeter for remote monitoring in this setting. OBJECTIVE: This study aimed to explore the use, performance, and acceptability of the pulse oximeter by clinicians and patients in rpavirtual during COVID-19. METHODS: Semistructured interviews and usability testing were conducted. Stable adult patients with COVID-19 (aged ≥18 years) who used the pulse oximeter and were monitored by rpavirtual, and rpavirtual clinicians monitoring these patients were interviewed. Clinicians could be nurses, doctors, or staff who were part of the team that assisted patients with the use of the pulse oximeter. Usability testing was conducted with patients who had the pulse oximeter when they were contacted. Interviews were coded using the Theoretical Framework of Acceptability. Usability testing was conducted using a think-aloud protocol. Data were collected until saturation was reached. RESULTS: Twenty-one patients (average age 51, SD 13 years) and 15 clinicians (average age 41, SD 11 years) completed the interview. Eight patients (average age 51, SD 13 years) completed the usability testing. All participants liked the device and thought it was easy to use. They also had a good understanding of how to use the device and the device's purpose. Patients' age and device use-related characteristics (eg, the warmth of hands and hand steadiness) were identified by users as factors negatively impacting the accurate use of the pulse oximeter. CONCLUSIONS: Patients and clinicians had very positive perceptions of the pulse oximeter for COVID-19 remote monitoring, indicating high acceptability and usability of the device. However, factors that may impact the accuracy of the device should be considered when delivering interventions using the pulse oximeter for remote monitoring. Targeted instructions about the use of the device may be necessary for specific populations (eg, older people and patients unfamiliar with technology). Further research should focus on the integration of the pulse oximeter data into electronic medical records for real-time and secure patient monitoring.

Process mining for healthcare: Characteristics and challenges.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Usability evaluation of an integrated electronic medication management system implemented in an oncology setting using the unified theory of the acceptance and use of technology.

BACKGROUND: Medication management processes in an Oncology setting are complex and difficult to examine in isolation from interrelated processes and contextual factors. This qualitative study aims to evaluate the usability of an Electronic Medication Management System (EMMS) implemented in a specialised oncology unit using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. METHODS: The study was conducted in a 12-bed outpatient Oncology unit of a major teaching hospital 6 months following implementation of a commercial EMMS. In-depth semi-structured interviews were conducted with doctors, nurses and pharmacists using the system to assess usability. The UTAUT framework was used to analyse the results, which facilitated evaluation of interrelated aspects and provided a structured summary of user experience and usability factors. RESULTS: Direct cross-comparison between user groups illustrated that doctors and pharmacists were generally satisfied with the facilitating conditions (hardware and training), but had divergent perceptions of performance (automation, standardised protocols and communication and documented) and effort (mental and temporal demand) expectancy. In counterpoint, nurses were generally satisfied across all constructs. Prior experience using an alternative EMMS influenced performance and effort expectancy and was related to early dissatisfaction with the EMMS. Furthermore, whilst not originally designed for the healthcare setting, the flexibility of the UTAUT allowed for translation to the hospital environment. CONCLUSION: Nurses demonstrated overall satisfaction with the EMMS, whilst doctors and pharmacists perceived usability problems, particularly related to restricted automaticity and system complexity, which hindered perceived EMMS success. The study demonstrates the feasibility and utility of the UTAUT framework to evaluate usability of an EMMS for multiple user groups in the Oncology setting.

Implementation of an Electronic Medication Management System in a large tertiary hospital: a case of qualitative inquiry.

BACKGROUND: Hospitals across Australia are implementing Clinical Information Systems, e.g. Electronic Medication Management Systems (EMMS) at a rapid pace to moderate health services. The benefits of the EMMS depend on the acceptance of the system by the clinicians. The study hospital used a unique patient-centric implementation strategy that was based on the guiding principle of "one patient, one chart" to avoid a patient being on a hybrid medication chart. This paper aims to study the factors facilitating or hindering the adoption of the EMMS as viewed by clinicians and the implementation team. METHODS: Four focus groups (FG), one each for (1) doctors, (2) nurses, (3) pharmacists, and (4) implementation team, were conducted. A guide for the FG was based on the Unified Theory of Acceptance and Use of Technology (UTAUT). RESULTS: A total of 23 unique subthemes were identified and were grouped into five main themes (1) implementation strategy, (2) organisational outcome of EMMS, (3) individual impact of EMMS, (4) IT product, and (5) organisational culture. Clinicians reported improvement in their workflow efficiency post-EMMS implementation. They also reported some challenges in using the EMMS that centered around the area of infrastructure, technical and design issues. Additionally, the implementation team highlighted two crucial factors influencing the success of EMMS implementation, namely: (1) the patient-centric implementation strategy, and (2) the organisation readiness. CONCLUSION: Overall, this study outlines the implementation process of the EMMS in a large healthcare facility from the clinicians' and the implementation team's perspectives using UTAUT model. The result suggests that clinicians' acceptance of the EMMS was highly influenced by the unique implementation strategy (namely, patient-centric approach and clinical leadership in the implementation team). Whereas the level of adoption of EMMS by clinicians was determined by their level of perceived and realised benefits. On the other hand, a number of barriers to the adoption of EMMS were discovered, namely, general training instead of customised training based on local needs, technical and design issues and lack of availability of computer systems. It is suggested that promptly resolving these issues can improve the adoption of the EMMS.

Co-expression of a cyclizing asparaginyl endopeptidase enables efficient production of cyclic peptides in planta.

Cyclotides are ultra-stable, backbone-cyclized plant defence peptides that have attracted considerable interest in the pharmaceutical industry. This is due to their range of native bioactivities as well as their ability to stabilize other bioactive peptides within their framework. However, a hindrance to their widespread application is the lack of scalable, cost-effective production strategies. Plant-based production is an attractive, benign option since all biosynthetic steps are performed in planta. Nonetheless, cyclization in non-cyclotide-producing plants is poor. Here, we show that cyclic peptides can be produced efficiently in Nicotiana benthamiana, one of the leading plant-based protein production platforms, by co-expressing cyclotide precursors with asparaginyl endopeptidases that catalyse peptide backbone cyclization. This approach was successful in a range of other plants (tobacco, bush bean, lettuce, and canola), either transiently or stably expressed, and was applicable to both native and engineered cyclic peptides. We also describe the use of the transgenic system to rapidly identify new asparaginyl endopeptidase cyclases and interrogate their substrate sequence requirements. Our results pave the way for exploiting cyclotides for pest protection in transgenic crops as well as large-scale production of cyclic peptide pharmaceuticals in plants.

Ensemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints.

BACKGROUND: Recent quality control of complex mixtures, including herbal medicines, is not limited to chemical chromatographic definition of one or two selected compounds; multivariate linear regression methods with dimension reduction or regularisation have been used to predict the bioactivity capacity from the chromatographic fingerprints of the herbal extracts. The challenge of this type of analysis requires a multi-dimensional approach at two levels: firstly each herb comprises complex mixtures of active and non-active chemical components; and secondly there are many factors relating to the growth, production, and processing of the herbal products. All these factors result in the significantly diverse concentrations of bioactive compounds in the herbal products. Therefore, it is imminent to have a predictive model with better generalisation that can accurately predict the bioactivity capacity of samples when only the chemical fingerprints data are available. RESULTS: In this study, the algorithm of Stacking Multivariate Linear Regression (SMLR) and a few other commonly used chemometric approaches were evaluated. They were to predict the Cluster of Differentiation 80 (CD80) expression bioactivity of a commonly used herb, Astragali Radix (AR), from the corresponding chemical chromatographic fingerprints. SMLR provides a superior prediction accuracy in comparison with the other multivariate linear regression methods of PCR, PLSR, OPLS and EN in terms of MSEtest and the goodness of prediction of test samples. CONCLUSIONS: SMLR is a better platform than some multivariate linear regression methods. The first advantage of SMLR is that it has better generalisation to predict the bioactivity capacity of herbal medicines from their chromatographic fingerprints. Future studies should aim to further improve the SMLR algorithm. The second advantage of SMLR is that single chemical compounds can be effectively identified as highly bioactive components which demands further CD80 bioactivity confirmation..

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm.

BACKGROUND: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. METHODS: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. RESULTS: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. CONCLUSIONS: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

The C-terminal propeptide of a plant defensin confers cytoprotective and subcellular targeting functions.

BACKGROUND: Plant defensins are small (45-54 amino acids), basic, cysteine-rich proteins that have a major role in innate immunity in plants. Many defensins are potent antifungal molecules and are being evaluated for their potential to create crop plants with sustainable disease resistance. Defensins are produced as precursor molecules which are directed into the secretory pathway and are divided into two classes based on the absence (class I) or presence (class II) of an acidic C-terminal propeptide (CTPP) of about 33 amino acids. The function of this CTPP had not been defined. RESULTS: By transgenically expressing the class II plant defensin NaD1 with and without its cognate CTPP we have demonstrated that NaD1 is phytotoxic to cotton plants when expressed without its CTPP. Transgenic cotton plants expressing constructs encoding the NaD1 precursor with the CTPP had the same morphology as non-transgenic plants but expression of NaD1 without the CTPP led to plants that were stunted, had crinkled leaves and were less viable. Immunofluorescence microscopy and transient expression of a green fluorescent protein (GFP)-CTPP chimera were used to confirm that the CTPP is sufficient for vacuolar targeting. Finally circular dichroism and NMR spectroscopy were used to show that the CTPP adopts a helical confirmation. CONCLUSIONS: In this report we have described the role of the CTPP on NaD1, a class II defensin from Nicotiana alata flowers. The CTPP of NaD1 is sufficient for vacuolar targeting and plays an important role in detoxification of the defensin as it moves through the plant secretory pathway. This work may have important implications for the use of defensins for disease protection in transgenic crops.

Field resistance to Fusarium oxysporum and Verticillium dahliae in transgenic cotton expressing the plant defensin NaD1.

The plant defensin NaD1, from Nicotiana alata, has potent antifungal activity against a range of filamentous fungi including the two important cotton pathogens, Fusarium oxysporum f. sp. vasinfectum (Fov) and Verticillium dahliae. Transgenic cotton plants expressing NaD1 were produced and plants from three events were selected for further characterization. Homozygous plants were assessed in greenhouse bioassays for resistance to Fov. One line (D1) was selected for field trial testing over three growing seasons in soils naturally infested with Fov and over two seasons in soils naturally infested with V. dahliae. In the field trials with Fov-infested soil, line D1 had 2-3-times the survival rate, a higher tolerance to Fov (higher disease rank), and a 2-4-fold increase in lint yield compared to the non-transgenic Coker control. When transgenic line D1 was planted in V. dahliae-infested soil, plants had a higher tolerance to Verticillium wilt and up to a 2-fold increase in lint yield compared to the non-transgenic Coker control. Line D1 did not exhibit any detrimental agronomic features compared to the parent Coker control when plants were grown in non-diseased soil. This study demonstrated that the expression of NaD1 in transgenic cotton plants can provide substantial resistance to two economically important fungal pathogens.

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS: A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS: The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION: This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.

OBJECTIVE: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead. METHODS: For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required. RESULTS: Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks. CONCLUSION: Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected. SIGNIFICANCE: To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.

A chimeric arabinogalactan protein promotes somatic embryogenesis in cotton cell culture.

Arabinogalactan proteins (AGPs) are a family of extracellular plant proteoglycans implicated in many aspects of plant growth and development, including in vitro somatic embryogenesis (SE). We found that specific AGPs were produced by cotton (Gossypium hirsutum) calli undergoing SE and that when these AGPs were isolated and incorporated into tissue culture medium, cotton SE was promoted. When the AGPs were partly or fully deglycosylated, SE-promoting activity was not diminished. Testing of AGPs separated by reverse-phase high-performance liquid chromatography revealed that the SE-promoting activity resided in a hydrophobic fraction. We cloned a full-length complementary DNA (cotton PHYTOCYANIN-LIKE ARABINOGALACTAN-PROTEIN1 [GhPLA1]) that encoded the protein backbone of an AGP in the active fraction. It has a chimeric structure comprising an amino-terminal signal sequence, a phytocyanin-like domain, an AGP-like domain, and a hydrophobic carboxyl-terminal domain. Recombinant production of GhPLA1 in tobacco (Nicotiana tabacum) cells enabled us to purify and analyze a single glycosylated AGP and to demonstrate that this chimeric AGP promotes cotton SE. Furthermore, the nonglycosylated phytocyanin-like domain from GhPLA1, which was bacterially produced, also promoted SE, indicating that the glycosylated AGP domain was unnecessary for in vitro activity.

Attaining consensus on a core dataset for upper limb lymphoedema using the Delphi method: A foundational step in creating a clinical support system.

Background: Lymphoedema is a condition of localised swelling caused by a compromised lymphatic system. The protein-rich fluid accumulating in the interstitial tissue can create inflammation and irreversible changes to the skin and underlying tissue. An array of methods has been used to assess and report these changes. Heterogeneity is evident in the clinic and in the literature for the domains assessed, outcomes and outcome measures selected, measurement protocols followed, methods of analysis, and descriptors used to report change. Objective: This study seeks consensus on the required items for inclusion in a core data set for upper limb lymphoedema to digitise the monitoring and reporting of upper limb lymphoedema. Methods: The breadth of outcomes and descriptors in common use were captured in prior studies by this research group. This list was refined by frequency and proposed to experts in the field (n = 70) through a two-round online modified Delphi study. These participants rated the importance of each item for inclusion in the dataset and identified outcomes or descriptors they felt were missing in Round 1. In Round 2, participants rated any new outcomes or descriptors proposed and preference for how numeric data is displayed. Results: The core dataset was confirmed on completion of Round 2. Interlimb difference as a percentage, and limb volume were preferred for graphed display over time; and descriptors for observed and palpated change narrowed from 42 to 20. Conclusion: This dataset provides the foundation to create a clinical support system for upper limb lymphoedema.

The Impact of an Electronic Medication Management System on Medication Deviations on Admission and Discharge from Hospital.

Medication errors at transition of care remain a concerning issue. In recent times, the use of integrated electronic medication management systems (EMMS) has caused a reduction in medication errors, but its effectiveness in reducing medication deviations at transition of care has not been studied in hospital-wide settings in Australia. The aim of this study is to assess medication deviations, such as omissions and mismatches, pre-EMMS and post-EMMS implementation at transition of care across a hospital. In this study, patient records were reviewed retrospectively to identify medication deviations (medication omissions and medication mismatches) at admission and discharge from hospital. A total of 400 patient records were reviewed (200 patients in the pre-EMMS and 200 patients in the post-EMMS group). Out of 400 patients, 112 in the pre-EMMS group and 134 patients in post-EMMS group met the inclusion criteria and were included in the analysis. A total of 105 out of 246 patients (42.7%) had any medication deviations on their medications. In the pre-EMMS group, 59 out of 112 (52.7%) patients had any deviations on their medications compared to 46 out of 134 patients (34.3%) from the post-EMMS group (p = 0.004). The proportion of patients with medication omitted from inpatient orders was 36.6% in the pre-EMMS cohort vs. 22.4% in the post-EMMS cohort (p = 0.014). Additionally, the proportion of patients with mismatches in medications on the inpatient charts compared to their medication history was 4.5% in the pre-EMMS group compared to 0% in the post-EMMS group (p = 0.019). Similarly, the proportion of patients with medications omitted from their discharge summary was 23.2% in the pre-EMMS group vs. 12.7% in the post-EMMS group (p = 0.03). Our study demonstrates a reduction in medication deviations after the implementation of the EMMS in hospital settings.

Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis.

BACKGROUND AND OBJECTIVE: The global market for AI systems used in lung tuberculosis (TB) detection has expanded significantly in recent years. Verifying their performance across diverse settings is crucial before medical organisations can invest in them and pursue safe, wide-scale deployment. The goal of this research was to synthesise the clinical evidence for the diagnostic accuracy of certified AI products designed for screening TB in chest X-rays (CXRs) compared to a microbiological reference standard. METHODS: Four databases were searched between June to September 2022. Data concerning study methodology, system characteristics, and diagnostic accuracy metrics was extracted and summarised. Study bias was evaluated using QUADAS-2 and by examining sources of funding. Forest plots for diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves were constructed for the AI products individually and collectively. RESULTS: 10 out of 3642 studies satisfied the review criteria however only 8 were subject to meta-analysis following bias assessment. Three AI products were evaluated with a 95 % confidence interval producing the following pooled estimates for accuracy rankings: qXR v2 (sensitivity of 0.944 [0.887-0.973], specificity of 0.692 [0.549-0.805], DOR of 3.63 [3.17-4.09], Lunit INSIGHT CXR v3.1 (sensitivity of 0.853 [0.787-0.901], specificity of 0.646 [0.627-0.665], DOR of 2.37 [1.96-2.78]), and CAD4TB v3.07 (sensitivity of 0.917 [0.848-0.956], specificity of 0.371 [0.336-0.408], DOR of 1.91 [1.4-2.47]). Overall, the products had a sensitivity of 0.903 (0.859-0.934), specificity of 0.526 (0.409-0.641), and DOR of 2.31 (1.78-2.84). CONCLUSION: Current publicly available evidence indicates considerable variability in the diagnostic accuracy of available AI products although overall they have high sensitivity and modest specificity which is improving with time. These preliminary results are limited by the small number of studies and poor coverage for low TB burden settings. More research is needed to expand the clinical evidence base for the performance of AI products.

The Plant Defensin Ppdef1 Is a Novel Topical Treatment for Onychomycosis.

Onychomycosis, or fungal nail infection, causes not only pain and discomfort but can also have psychological and social consequences for the patient. Treatment of onychomycosis is complicated by the location of the infection under the nail plate, meaning that antifungal molecules must either penetrate the nail or be applied systemically. Currently, available treatments are limited by their poor nail penetration for topical products or their potential toxicity for systemic products. Plant defensins with potent antifungal activity have the potential to be safe and effective treatments for fungal infections in humans. The cystine-stabilized structure of plant defensins makes them stable to the extremes of pH and temperature as well as digestion by proteases. Here, we describe a novel plant defensin, Ppdef1, as a peptide for the treatment of fungal nail infections. Ppdef1 has potent, fungicidal activity against a range of human fungal pathogens, including Candida spp., Cryptococcus spp., dermatophytes, and non-dermatophytic moulds. In particular, Ppdef1 has excellent activity against dermatophytes that infect skin and nails, including the major etiological agent of onychomycosis Trichophyton rubrum. Ppdef1 also penetrates human nails rapidly and efficiently, making it an excellent candidate for a novel topical treatment of onychomycosis.

Understanding unintended consequences for EMR: a literature review.

While Electronic Medical Records (EMR) have been hailed as an important step for advancing healthcare, a number of studies have noted that its introduction also brings unintended consequences to healthcare organisations. This means that introducing EMR to key stakeholders such as clinicians, healthcare administrators, as well as to the overall healthcare organisations, may not be as straightforward as we have hoped for. There has been some empirical work and systematic reviews specifically addressing the unintended consequences for EMR. Given the complexity of these issues, continued effort to investigate them is critical. This paper first proposes an integration and systematisation of the existing literature on the unintended consequences of EMR (including its various definitions and classifications), and then provides insights for dealing with these issues, including mitigation strategies for addressing these issues.

Patient and clinician use characteristics and perceptions of pulse oximeter use: A scoping review.

BACKGROUND AND OBJECTIVES: The need to monitor patients outside of a formal clinical setting, such as a hospital or ambulatory care facility, has become increasingly important since COVID-19. It introduces significant challenges to ensure accurate and timely measurements, maintain strong patient engagement, and operationalise data for clinical decision-making. Remote Patient Monitoring (RPM) devices like the pulse oximeter help mitigate these difficulties, however, practical approaches to successfully integrate this technology into existing patient-clinician interactions that ensure the delivery of safe and effective care are vital. The objective of this scoping review was to synthesise existing literature to provide an overview of the variety of user perceptions associated with pulse oximeter devices, which may impact patients' and clinicians' acceptance of the devices in a RPM context. METHODS: A search over three databases was conducted between April 2021 - June 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. A total of 16 articles were included in this scoping review. RESULTS: Results indicate there has been an increase in use of pulse oximeters across hospital and community settings for continuous vital signs monitoring and remote monitoring of patients over time. Research in this area is shifting towards increasing accessibility of care through the development and implementation of telehealth systems and phone oximeters. Aspects of pulse oximeter UX most frequently investigated are usability and acceptability, however, these terms are often undefined, or definitions vary across studies. Perceived effectiveness, opportunity costs, and attitude towards use remain unexplored areas of UX. Overall, patients and clinicians view the pulse oximeter positively and find it user-friendly. A high level of learnability was found for the device and additional benefits included increasing patient self-efficacy and clinician motivation to work. However, issues getting an accurate reading due to device usability are still experienced by some patients and clinicians. CONCLUSION: This scoping review is the first to summarise user perceptions of the pulse oximeter in a healthcare context. It showed that both patients and clinicians hold positive perceptions of the pulse oximeter and important factors to consider in designing user-focused services include ease-of-use and wearability of devices; context of use including user's prior health and IT knowledge; attitude towards use and perceived effectiveness; impact on user motivation and self-efficacy; and finally, potential user costs like inconvenience or increased anxiety. With the rapid increase in research studies examining pulse oximeter use for RPM since COVID-19, a systematic review is warranted as the next step to consolidate evidence and investigate the impact of these factors on pulse oximeter acceptance and effectiveness.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

A novel method for clinical risk prediction with low-quality data.

In real-world data, predictive models for clinical risks (such as adverse drug reactions, hospital readmission, and chronic disease onset) are constantly struggling with low-quality issues, namely redundant and highly correlated features, extreme category imbalances, and most importantly, a large number of missing values. In most existing work, each patient is represented as a value vector with the fixed-length from some feature space, and missing values are forced to be imputed, which introduces much noise for prediction if the data set is highly incomplete. Besides, other challenges are either remaining unresolved or only partially solved when modeling, but without a systematic approach. In this paper, we propose a novel framework to address these low-quality problems, that we first treat patients as bags with the various number of feature-value pairs, called instances, and map them to an embedding space through our proposed feature embedding method to learn from it directly. In this way, predictive models can avoid the negative impact of missing data naturally. A novel multi-instance neural network is then connected, using two computational modules to deal with the problems of correlated and redundant features: multi-head attention and attention-based multi-instance pooling. They are capable of capturing the instance correlations and locating valuable information in each instance or bag. The feature embedding and multi-instance neural network are parameterized and optimized jointly in an end-to-end manner. Moreover, the training process is under both main and auxiliary supervision with focal loss functions to avoid the caveat of a highly imbalanced label set. This proposed framework is named AMI-Net3. We evaluate it on three suitable data sets from real-world settings with different clinical risk prediction tasks: adverse drug reaction of risperidone, schizophrenia relapse, and invasive fungi infection, respectively. The comprehensive experimental results demonstrate the effectiveness and superiority of our proposed method over competitive baselines.