Utilization of phase I studies for target validation of first-in-class drugs.

This review discusses the growing importance of target validation within phase I (P1) trials as a new trend in drug development, especially in establishing proof of concept (POC) for first-in-class drugs. The paper describes two approaches: the P1-PIV approach, which directly evaluates the primary end point for a pivotal clinical study to confirm therapeutic effects during P1, and the newly introduced P1-FCTE, which assesses functional changes necessary for therapeutic effect as a novel target validation milestone in P1. By providing practical examples of first-in-class drugs, we compare the benefits, costs, hurdles and applicable therapeutic areas of these approaches. Finally, we discuss the potential of these novel approaches to facilitate POC success, shorten development timelines and ultimately increase drug discovery success rates.

Phase-change ultrasound contrast agents for proton range verification: towards anin vivoapplication.

Objective.In proton therapy, range uncertainties prevent optimal benefit from the superior depth-dose characteristics of proton beams over conventional photon-based radiotherapy. To reduce these uncertainties we recently proposed the use of phase-change ultrasound contrast agents as an affordable and effective range verification tool. In particular, superheated nanodroplets can convert into echogenic microbubbles upon proton irradiation, whereby the resulting ultrasound contrast relates to the proton range with high reproducibility. Here, we provide a firstin vivoproof-of-concept of this technology.Approach.First, thein vitrobiocompatibility of radiation-sensitive poly(vinyl alcohol) perfluorobutane nanodroplets was investigated using several colorimetric assays. Then,in vivoultrasound contrast was characterized using acoustic droplet vaporization (ADV) and later using proton beam irradiations at varying energies (49.7 MeV and 62 MeV) in healthy Sprague Dawley rats. A preliminary evaluation of thein vivobiocompatibility was performed using ADV and a combination of physiology monitoring and histology.Main results.Nanodroplets were non-toxic over a wide concentration range (<1 mM). In healthy rats, intravenously injected nanodroplets primarily accumulated in the organs of the reticuloendothelial system, where the lifetime of the generated ultrasound contrast (<30 min) was compatible with a typical radiotherapy fraction (<5 min). Spontaneous droplet vaporization did not result in significant background signals. Online ultrasound imaging of the liver of droplet-injected rats demonstrated an energy-dependent proton response, which can be tuned by varying the nanodroplet concentration. However, caution is warranted when deciding on the exact nanodroplet dose regimen as a mild physiological response (drop in cardiac rate, granuloma formation) was observed after ADV.Significance.These findings underline the potential of phase-change ultrasound contrast agents forin vivoproton range verification and provide the next step towards eventual clinical applications.

Noninvasive biomarkers for the detection of GERD-induced pulmonary injury.

BACKGROUND: The role of gastroesophageal reflux in progressive lung damage is increasingly recognized. We have proposed, based on our work with lung transplant recipients, a novel immune mechanism of pulmonary injury after aspiration of gastric contents, during which higher levels of normally sequestered lung self-antigens (SAgs) collagen V (Col-V) and K-alpha-1 tubulin (Kα1T) in circulating small extracellular vesicles (EVs) induce the production of self-antibodies (SAbs) anti-Col-V and anti-Kα1T. Thus, we aimed to determine whether levels of SAbs or SAgs increased in an animal model of aspiration-induced lung damage in a nontransplant setting. METHODS: We created a murine model of repetitive lung aspiration using C57BL/6J mice. Mice were aspirated weekly with 1 mL/kg of hydrochloric acid (n = 9), human gastric contents (n = 9), or combined (1:1) fluid (n = 9) once, three, or six times (n = 3 in each subgroup; control group, n = 9). Blood samples were periodically obtained, and all animals were sacrificed at day 90 for pathological assessment. SAbs were measured using an enzyme-linked immunosorbent assay; SAgs and NF-κB contained in small EVs were assessed by western blot. RESULTS: Aspirated mice weighed significantly less than controls throughout the study and had histological evidence of pulmonary injury at day 90. Overall, aspirated mice developed higher concentrations of anti-Col-V at day 28 (53.9 ± 28.7 vs. 29.9 ± 4.5 ng/mL, p < 0.01), day 35 (42.6 ± 19.8 vs. 28.6 ± 7.2 ng/mL, p = 0.038), and day 90 (59.7 ± 27.7 vs. 34.1 ± 3.2 ng/mL, p = 0.014) than the control group. Circulating small EVs isolated from aspirated mice on day 90 contained higher levels of Col-V (0.7 ± 0.56 vs. 0.18 ± 0.6 m.o.d., p = 0.009) and NF-κB (0.42 ± 0.27 vs. 0.27 ± 0.09 m.o.d., p = 0.095) than those from controls. CONCLUSIONS: This experimental study supports the theory that gastroesophageal reflux leads to the development of lung damage and an increase of humoral markers that may serve as noninvasive biomarkers to detect asymptomatic lung injury among patients with gastroesophageal reflux disease.

Optical Interferometric Device for Rapid and Specific Detection of Biological Cells.

Here, we report a rapid and accurate optical method for detecting cells from liquid samples in a label-free manner. The working principle of the method is based on the interference of parts of a conical laser beam, coming from a single-mode optical fiber directly, and reflected from a flat glass surface. The glass is functionalized by antibodies against the cells to be detected from the liquid sample. Cells bound to that surface modify the reflected beam, and hence, change the resulting interference pattern, too. By registering and interpreting the variation in the image, the presence of cells from the sample can be detected. As for a demonstration, cell suspensions from a U937 cell line were used in glass chambers functionalized by antibodies (TMG6-5 (mIgG1)) to which the cells specifically bind. The limit of detection (LOD) of the method was also estimated. This proof-of-concept setup offers a cost-effective and easy-to-use way of rapid and specific detection of any type of cells (including pathogens) from suspensions (e.g., body fluids). The possible portability of the device predicts its applicability as a rapid test in clinical diagnostics.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach.

BACKGROUND: Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. OBJECTIVE: The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. METHODS: We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. RESULTS: Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. CONCLUSIONS: We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

Diagnostic performance of light reflex pupillometry in Alzheimer's disease.

Easily applied diagnostic tools such as digital biomarkers for Alzheimer's disease (AD) are urgently needed due to the recent approval of disease-modifying therapies. We aimed to determine the diagnostic performance of hand-held, quantitative light reflex pupillometry (qLRP) in patients with AD in a proof-of-concept, cross-sectional study. Participants underwent qLRP at a university memory clinic from August 2022 to October 2023. We fitted multivariable logistic regression models with qLRP, sex, and age as predictors evaluated with area under the receiver operating characteristics curve (AUROC). In total, 107 patients with AD, 44 patients with mixed AD and vascular cognitive dysfunction (VCD), 53 patients with dementia with Lewy bodies (DLB), and 50 healthy controls (HCs) were included. Our diagnostic models showed similar discriminatory ability (AUROC range 0.74-0.81) when distinguishing patients with AD from HCs and other dementias. The qLRP seems promising as a bedside digital biomarker to aid in diagnosing AD. Highlights: We demonstrated the diagnostic performance of qLRP in Alzheimer's disease.The diagnostic models were robust in sensitivity analyses.qLRP may assist in the bedside diagnostic evaluation of Alzheimer's disease.

Strategies for successful dose optimization in oncology drug development: a practical guide.

Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program.

A novel in vitro cell model of the proteinase/antiproteinase balance observed in alpha-1 antitrypsin deficiency.

Background: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition resulting from mutations in the alpha-1 antitrypsin (AAT) protein, a major systemic antiproteinase, resulting in reduced/no release of AAT, disrupting the proteinase/antiproteinase balance. A sustained imbalance can cause structural changes to the lung parenchyma, leading to emphysema. Predicting and assessing human responses to potential therapeutic candidates from preclinical animal studies have been challenging. Our aims were to develop a more physiologically relevant in vitro model of the proteinase/antiproteinase balance and assess whether the data generated could better predict the efficacy of pharmacological candidates to inform decisions on clinical trials, together with expected biomarker responses. Methods: We developed an in vitro model assessing the proteinase/antiproteinase balance by the changes in the fibrinogen cleavage products of neutrophil elastase (NE) and proteinase 3 (PR3). This allowed the assessment of physiological and pharmaceutical neutrophil serine proteinase (NSP) inhibitors to determine the putative threshold at which the maximal effect is achieved. Results: AAT significantly reduced NE and PR3 activity footprints, with the maximal reduction achieved at concentrations above 10 µM. The inhibitor MPH966 alone also significantly reduced NE footprint generation in a concentration-dependent manner, leveling out above 100 nM but had no effect on the PR3 footprint. At levels of AAT consistent with AATD, MPH966 had an additive effect, reducing the NE activity footprint more than either inhibitor alone. Conclusion: Our results support an inhibitor threshold above which the activity footprint generation appears resistant to increasing dosage. Our model can support the testing of inhibitors, confirming activity biomarkers as indicators of likely pharmaceutical efficacy, the assessment of NSP activity in the pathophysiology of emphysema, and the likely function of biological or pharmacological inhibitors in disease management.

Proof-of-Concept Trial of a Tablet-based Program in Pediatrics to Motivate Parental Use of an Online Behavioral Parent Training Program.

Internet-based parent training is a promising intervention approach for child disruptive behavior. However, engagement in these interventions is limited. The Parenting Young Children Check-up (PYCC) was designed to improve engagement in internet-based parent training programs via three components: 1) an initial check-up, 2) text messages, and 3) a website. This proof-of-concept trial used feedback from parents and pediatric clinic staff to evaluate feasibility as well as the extent to which the initial check-up was associated with behavioral intentions to use the PYCC website. Pediatric staff and parents rated the PYCC highly, and parents reported interest in using the PYCC website.

Off-label use of clomiphene citrate to treat anabolic androgenic steroid induced hypogonadism upon cessation among men (CloTASH) - A pilot study protocol.

Background: Non-prescribed anabolic androgenic steroid (AAS) use is associated with AAS-induced hypogonadism (ASIH), and metabolic, cardiovascular, and mental health risks. Symptoms of ASIH (fatigue, depression, anxiety, sexual dysfunction) are hard to endure following cessation, but there is no consensus on whether endocrine treatment should be used to treat ASIH. This proof-of-concept study aims to explore safety of off-label clomiphene citrate therapy, whether the treatment will reduce the symptoms of androgen deficiency, and to study changes in health risks after cessation. Methods: In this open-labeled non-randomized off-label hormone intervention pilot study, we shall include males with AAS dependence intending to cease use. The 16-week intervention included clomiphene citrate, transdermal testosterone gel for the first four weeks and optional human chorionic gonadotropin (hCG) from week 4 if low treatment response. Measures of physical and mental health will be examined from ongoing AAS use, during the intervention, and at 6- and 12 months post cessation. Change in self-reported symptoms of hypogonadism and other withdrawal symptoms will be compared with data from a group of men who ended AAS use temporarily without the medical intervention. The study may provide valuable clinical insights and may be used to inform the design of future intervention studies.

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study.

BACKGROUND: We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. OBJECTIVE: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. METHODS: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures-total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. RESULTS: We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. CONCLUSIONS: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model.

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

Practical QSP application from the preclinical phase to enhance the probability of clinical success: Insights from case studies in oncology.

Quantitative Systems Pharmacology (QSP) has emerged as a promising modeling and simulation (M&S) approach in drug development, with potential to improve clinical success rates. While conventional M&S has significantly contributed to quantitative understanding in late preclinical and clinical phases, it falls short in explaining unexpected phenomena and testing hypotheses in the early research phase. QSP presents a solution to these limitations. To harness the full potential of QSP in early preclinical stages, preclinical modelers who are familiar with conventional M&S need to update their understanding of the differences between conventional M&S and QSP. This review focuses on QSP applications during the preclinical stage, citing case examples and sharing our experiences in oncology. We emphasize the critical role of QSP in increasing the probability of success for clinical proof of concept (PoC) when applied from the early preclinical stage. Enhancing the quality of both hypotheses and QSP models from early preclinical stage is of critical importance. Once a QSP model achieves credibility, it facilitates predictions of clinical responses and potential biomarkers. We propose that sequential QSP applications from preclinical stages can improve success rates of clinical PoC, and emphasize the importance of refining both hypotheses and QSP models throughout the process.

Coronary bypass surgery guided by computed tomography in a low-risk population.

BACKGROUND AND AIMS: In patients with three-vessel disease and/or left main disease, selecting revascularization strategy based on coronary computed tomography angiography (CCTA) has a high level of virtual agreement with treatment decisions based on invasive coronary angiography (ICA). METHODS: In this study, coronary artery bypass grafting (CABG) procedures were planned based on CCTA without knowledge of ICA. The CABG strategy was recommended by a central core laboratory assessing the anatomy and functionality of the coronary circulation. The primary feasibility endpoint was the percentage of operations performed without access to the ICA. The primary safety endpoint was graft patency on 30-day follow-up CCTA. Secondary endpoints included topographical adequacy of grafting, major adverse cardiac and cerebrovascular (MACCE), and major bleeding events at 30 days. The study was considered positive if the lower boundary of confidence intervals (CI) for feasibility was ≥75% (NCT04142021). RESULTS: The study enrolled 114 patients with a mean (standard deviation) anatomical SYNTAX score and Society of Thoracic Surgery score of 43.6 (15.3) and 0.81 (0.63), respectively. Unblinding ICA was required in one case yielding a feasibility of 99.1% (95% CI 95.2%-100%). The concordance and agreement in revascularization planning between the ICA- and CCTA-Heart Teams was 82.9% with a moderate kappa of 0.58 (95% CI 0.50-0.66) and between the CCTA-Heart Team and actual treatment was 83.7% with a substantial kappa of 0.61 (95% CI 0.53-0.68). The 30-day follow-up CCTA in 102 patients (91.9%) showed an anastomosis patency rate of 92.6%, whilst MACCE was 7.2% and major bleeding 2.7%. CONCLUSIONS: CABG guided by CCTA is feasible and has an acceptable safety profile in a selected population of complex coronary artery disease.

A Novel Ear Impression-Taking Method Using Structured Light Imaging and Machine Learning: A Pilot Proof of Concept Study with Patients' Feedback on Prototype.

INTRODUCTION: Taking an ear impression is a minimally invasive procedure. A review of existing literature suggests that contactless methods of scanning the ear have not been developed. We proposed to establish a correlation between external ear features with the ear canal and with this proof of concept to develop a prototype and an algorithm for capturing and predicting ear canal information. METHODS: We developed a novel prototype using structured light imaging to capture external images of the ear. Using a large database of existing ear impression images obtained by traditional methods, correlation analyses were carried out and established. A deep neural network was devised to build a predictive algorithm. Patients undergoing hearing aid evaluation undertook both methods of ear impression-taking. We evaluated their subjective feedback and determined if there was a close enough objective match between the images obtained from the impression techniques. RESULTS: A prototype was developed and deployed for trial, and most participants were comfortable with this novel method of ear impression-taking. Partial matching of the ear canal could be obtained from the images taken, and the predictive algorithm applied for a few sample images was within good standard of error with proof of concept established. DISCUSSION: Further studies are warranted to strengthen the predictive capabilities of the algorithm and determine optimal prototype imaging positions so that sufficient ear canal information can be obtained for three-dimensional printing. Ear impression-taking may then have the potential to be automated, with the possibility of same-day three-dimensional printing of the earmold to provide timely access.

Designing, Developing, and Testing a Chatbot for Parents and Caregivers of Children and Young People With Rheumatological Conditions (the IMPACT Study): Protocol for a Co-Designed Proof-of-Concept Study.

BACKGROUND: Pediatric rheumatology is a term that encompasses over 80 conditions affecting different organs and systems. Children and young people with rheumatological chronic conditions are known to have high levels of mental health problems and therefore are at risk of poor health outcomes. Clinical psychologists can help children and young people manage the daily difficulties of living with one of these conditions; however, there are insufficient pediatric psychologists in the United Kingdom. We urgently need to consider other ways of providing early, essential support to improve their current well-being. One way of doing this is to empower parents and caregivers to have more of the answers that their children and young people need to support them further between their hospital appointments. OBJECTIVE: The objective of this co-designed proof-of-concept study is to design, develop, and test a chatbot intervention to support parents and caregivers of children and young people with rheumatological conditions. METHODS: This study will explore the needs and views of children and young people with rheumatological conditions, their siblings, parents, and caregivers, as well as health care professionals working in pediatric rheumatology. We will ask approximately 100 participants in focus groups where they think the gaps are in current clinical care and what ideas they have for improving upon them. Creative experience-based co-design workshops will then decide upon top priorities to develop further while informing the appearance, functionality, and practical delivery of a chatbot intervention. Upon completion of a minimum viable product, approximately 100 parents and caregivers will user-test the chatbot intervention in an iterative sprint methodology to determine its worth as a mechanism for support for parents. RESULTS: A total of 73 children, young people, parents, caregivers, and health care professionals have so far been enrolled in the study, which began in November 2023. The anticipated completion date of the study is April 2026. The data analysis is expected to be completed in January 2026, with the results being published in April 2026. CONCLUSIONS: This study will provide evidence on the accessibility, acceptability, and usability of a chatbot intervention for parents and caregivers of children and young people with rheumatological conditions. If proven useful, it could lead to a future efficacy trial of one of the first chatbot interventions to provide targeted and user-suggested support for parents and caregivers of children with chronic health conditions in health care services. This study is unique in that it will detail the needs and wants of children, young people, siblings, parents, and caregivers to improve the current support given to families living with pediatric rheumatological conditions. It will be conducted across the whole of the United Kingdom for all pediatric rheumatological conditions at all stages of the disease trajectory. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57238.

Effects of proof-of-concept training on reducing work stress and anxiety of junior medical staff: a randomized controlled trial proof-of-concept.

BACKGROUND: Psychological stress and anxiety have seriously affected the ability of new clinicians to adapt and coordinate their clinical work. Traditional pre-job training is often not very good at assisting new recruits to regulate their emotional problems. METHODS: This study is a randomized controlled study. A total of 435 newly recruited clinicians participated in the study. 428 clinicians were randomized into a control group (n = 214) and an intervention group (n = 214). The control group conducted regular pre-job training. Doctors of the intervention group attend a themed course every two weeks on the basis of regular induction training. Their physiological status was evaluated by Perceived Stress Scale (PPS-10), Generalized Anxiety Scale (GAD-7) and Psychological Resilience Scale (CD-RISC-10) 3 months later. Participants in the intervention group received a training satisfaction questionnaire. RESULTS: After entering the clinic for 3 months, the PSS-10 and GAD-7 scores of the intervention group were significantly lower than that of the control group. Consistently, the CD-RISC-10 score of new clinicians who received proof-of-concept pre-job training was significantly higher than that of new doctors in the control group. CONCLUSION: New doctors received the proof-of-concept group experienced alleviation in stress and anxiety.

Experimental validation off-line a nonlinear controller for removal of chromium using non-living cells of Yarrowia lipolytica.

The removal of hexavalent chromium [Cr (VI)] using non-living cells of Yarrowia lipolytica was investigated. Batch and continuous studies on removal of Cr (VI) achieved 97% and 99% removal from aqueous phase, respectively. The specific uptake values at pH of 2 in batch process were 40.73 ± 1.3 mg/g and 30.09 ± 0.23 mg/g on non-living cells, when 100 and 200 mg/L of metal Cr (VI) concentrations were used. In order to investigate the regulation of Cr (VI) under continuous operation based on reaction volume numerically a new class of feedback controller from structure polynomial was designed. The proposed methodology was used to an experimentally kinetic model for a removal Cr (VI) from Yarrowia lipolytica biomass was showed satisfactory closed-loop performance the proposed controller. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the methodology required to could be suitable for implementation in real time. In our experimental results, we highlight some discrepancies between simulation and reality despite these differences, the controller managed to perform convergence to removal Cr (VI). Finally, the results validated with off-line samples suggest that the proposed control could be suitable for in application in potential scenarios for wastewater treatment.

Data extraction for evidence synthesis using a large language model: A proof-of-concept study.

Data extraction is a crucial, yet labor-intensive and error-prone part of evidence synthesis. To date, efforts to harness machine learning for enhancing efficiency of the data extraction process have fallen short of achieving sufficient accuracy and usability. With the release of large language models (LLMs), new possibilities have emerged to increase efficiency and accuracy of data extraction for evidence synthesis. The objective of this proof-of-concept study was to assess the performance of an LLM (Claude 2) in extracting data elements from published studies, compared with human data extraction as employed in systematic reviews. Our analysis utilized a convenience sample of 10 English-language, open-access publications of randomized controlled trials included in a single systematic review. We selected 16 distinct types of data, posing varying degrees of difficulty (160 data elements across 10 studies). We used the browser version of Claude 2 to upload the portable document format of each publication and then prompted the model for each data element. Across 160 data elements, Claude 2 demonstrated an overall accuracy of 96.3% with a high test-retest reliability (replication 1: 96.9%; replication 2: 95.0% accuracy). Overall, Claude 2 made 6 errors on 160 data items. The most common errors (n = 4) were missed data items. Importantly, Claude 2's ease of use was high; it required no technical expertise or labeled training data for effective operation (i.e., zero-shot learning). Based on findings of our proof-of-concept study, leveraging LLMs has the potential to substantially enhance the efficiency and accuracy of data extraction for evidence syntheses.

Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.