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BACKGROUND: Clinical drug trials are intricate, involving numerous stakeholders, substantial data, and stringent regulations. Traditional systems for recording, storing, and sharing trial data often face data integrity, transparency, security, and interoperability challenges. The utilization of blockchain technology has emerged as a transformative influence in various industries, and its potential within healthcare, particularly in clinical drug trials, is increasingly gaining recognition. METHODS: Blockchain technology presents a decentralized and immutable ledger system that holds promise in effectively addressing these challenges. As the healthcare industry continues its journey of digital transformation, the incorporation of blockchain technology for monitoring clinical drug trials represents a paradigm shift that can result in more reliable, efficient, and transparent trials. RESULTS AND CONCLUSION: This review explores the innovative application of blockchain technology in transforming the monitoring and management of clinical drug trials and provides a comprehensive overview of the possibilities, challenges, and future directions of blockchain-based monitoring in the context of clinical drug trials, contributing to the progress of both blockchain technology and healthcare research practices.

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The harms caused by ultraviolet (UV) and blue light to eyes are attracting momentous concern due to growing exposure to artificial illumination and modern IT devices. Herein, a simple and eco-friendly adsorption approach was employed to integrate curcumin, a natural bioactive compound, into the cellulose substrate for the development of flexible and biodegradable filters capable of blocking harmful light. The curcumin/cellulose films demonstrate excellent UV-screening competence and photostability, with UV-A and UV-B screening ratios ranging from 92.8 % to 100 % and 89.2 % to 100 %, respectively. The films could block >96 % of blue light in the wavelength range of 400-500 nm. Meanwhile, the films maintain high transmittance (85.2-89.4 %) and low haze (2.0-2.7 %). The films can efficiently block blue light emanated from sunlight, light-emitting diodes, lighting systems, computer and mobile phone screens. Encouragingly, the incorporation of curcumin led to a substantial increase in the water contact angle, elevating it from 41.6 to 81.3°. Furthermore, the films exhibit excellent antimicrobial properties, biodegradability, and tensile strength in excess of 72 MPa. Therefore, these films fabricated entirely from natural resources have the potential to achieve practical applications such as food packaging and spectacle lens, especially suitable for electronic screen protectors.

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Complete and transparent reporting of randomized controlled trial publications (RCTs) is essential for assessing their credibility. We aimed to develop text classification models for determining whether RCT publications report CONSORT checklist items. Using a corpus annotated with 37 fine-grained CONSORT items, we trained sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. We assessed the impact of data augmentation methods (Easy Data Augmentation (EDA), UMLS-EDA, text generation and rephrasing with GPT-4) on model performance. We also fine-tuned section-specific PubMedBERT models (e.g., Methods) to evaluate whether they could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC). Fine-tuned PubMedBERT model that uses the sentence along with the surrounding sentences and section headers yielded the best overall performance (sentence level: 0.71 micro-F1, 0.67 macro-F1; article-level: 0.90 micro-F1, 0.84 macro-F1). Data augmentation had limited positive effect. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model improved recognition of methodology items, other section-specific models did not have significant impact. Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks.

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As an important biodegradable and partially biobased copolyester, poly(butylene succinate-co-terephthalate) (PBST) possesses comparable thermal and mechanical properties and superior gas barrier performance when compared with poly(butylene adipate-co-terephthalate) (PBAT), but it was found to display poorer melt processability during pelletizing and injection molding. To make clear its melt crystallization behavior under rapid cooling, PBST48 and PBST44 were synthesized, and their melt crystallization was investigated comparatively with PBAT48. PBST48 showed a PBAT48-comparable melt crystallization performance at a cooling rate of 10 °C/min or at isothermal conditions, but it showed a melt crystallization ability at a cooling rate of 40 °C/min which was clearly poorer. PBST44, which has the same mass composition as PBAT48, completely lost its melt crystallization ability under the rapid cooling. The weaker chain mobility of PBST, resulting from its shorter succinate moiety, is responsible for its inferior melt crystallization ability and processability. In comparison with PBAT48, PBST48 displayed higher tensile modulus, and both PBST48 and PBST44 showed higher light transmittance. The findings in this study deepen the understanding of PBST's properties and will be of guiding significance for improving PBST's processability and application development.

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Although extensive research has been carried out on collocation processing, it is still unclear how cross-language overlap and transparency influence the processing of collocations by L2 learners. In the current study, a phrase judgment task was used to investigate the processing of congruent (i.e., exist in both English and Arabic) and incongruent collocations (i.e., exist only in English) by Arabic non-native speakers of English. The semantic transparency of the items was controlled for. Results demonstrated the effect of congruency on processing: congruent items yielded more correct responses and faster response times than incongruent items. The effect of congruency was modulated by proficiency, with congruency having a stronger effect on lower-proficiency learners than higher-proficiency learners. Transparency had no effect, with no differences in response times and accuracy between transparent and opaque collocations. The findings have implications for the learning and teaching of L2 collocations.

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In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.

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BACKGROUND: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care. METHODS: In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design. RESULTS: After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights. CONCLUSION: This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

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Concern has been raised about the effectiveness of the Hospital Price Transparency Rule to facilitate a clear understanding of health care prices due to poor reporting by hospitals. However, the relationship between what services the hospital provides and what prices they report is not clear. We assessed reported prices in the Turquoise Health database and compared them at the hospital level with the CMS Provider of Services File to identify if a shoppable service was provided at a hospital. We found significant mismatch between the hospital prices being reported and the services being provided. For example, 56% of hospitals providing at least 1 shoppable service that requires public price reporting did not report any prices. Of hospitals reporting prices, most hospitals (66%) reported prices for only a portion of the services they provide. In addition, 12% of hospitals reported prices for services they do not provide. Only 6% of hospitals had complete concordance with price reporting and services they actually provide. Current compliance enforcement and penalties do not appear to be adequate to achieve the goals of the Hospital Price Transparency Rule.

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Transparency in healthcare organizations is essential for creating a culture of patient-centered care where patients are respected, informed, and actively engaged in their health and well-being. Organizational transparency is a crucial element in healthcare, enhancing patient safety and quality improvement. Transparency involves open communication about healthcare organizations' performance, outcomes, and processes, leading to improved accountability, trust, and patient engagement. Transparent organizations prioritize patient-centered care, involving patients in decision-making and fostering shared mental models between healthcare providers and patients. Psychological safety is vital for organizational transparency. Patient safety reporting systems play a key role in transparency, allowing anonymous reporting of safety concerns and incidents. These systems facilitate early risk identification, continuous improvement, and compliance with regulatory requirements. Transparency in reporting encourages a culture of openness, learning from near misses, and addressing systemic issues and human errors. It aligns with ethical principles, potentially mitigating legal challenges. This review synthesizes key themes, including the importance of patient-centered care, the role of psychological safety in fostering transparency, and the effectiveness of patient safety reporting systems.

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PURPOSE: There is increasing recognition of the importance of transparency and reproducibility in scientific research. This study aimed to quantify the extent to which programming code is publicly shared in pharmacoepidemiology, and to develop a set of recommendations on this topic. METHODS: We conducted a literature review identifying all studies published in Pharmacoepidemiology and Drug Safety (PDS) between 2017 and 2022. Data were extracted on the frequency and types of programming code shared, and other key open science practices (clinical codelist sharing, data sharing, study preregistration, and stated use of reporting guidelines and preprinting). We developed six recommendations for investigators who choose to share code and gathered feedback from members of the International Society for Pharmacoepidemiology (ISPE). RESULTS: Programming code sharing by articles published in PDS ranged from 1.8% in 2017 to 9.5% in 2022. It was more prevalent among articles with a methodological focus, simulation studies, and papers which also shared record-level data. CONCLUSION: Programming code sharing is rare but increasing in pharmacoepidemiology studies published in PDS. We recommend improved reporting of whether code is shared and how available code can be accessed. When sharing programming code, we recommend the use of permanent digital identifiers, appropriate licenses, and, where possible, adherence to good software practices around the provision of metadata and documentation, computational reproducibility, and data privacy.

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INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exception-tolerant hierarchical knowledge bases (i.e., knowledge bases, where rule-based knowledge is represented on several levels of abstraction), privacy concerns have not been addressed extensively in this context yet. However, privacy plays an important role, especially for medical applications. METHODS: When parts of the original dataset can be restored from a learned knowledge base, there may be a practically and legally relevant risk of re-identification for individuals. In this paper, we study privacy issues of exception-tolerant hierarchical knowledge bases which are learned from data. We propose approaches for determining and eliminating privacy issues of the learned knowledge bases. RESULTS: We present results for synthetic as well as for real world datasets. CONCLUSION: The results show that our approach effectively prevents privacy breaches while only moderately decreasing the inference quality.

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Objectives: To assess the effects of the transparent online open procurement arrangement on the prices, volumes, and costs of medicines in Ningxia, China. Methods: Data were extracted from the Ningxia pharmaceutical procurement platform, covering 16 months of purchase orders (December 2019 to March 2021) prior to the implementation of the transparent online open procurement policy and 20 months of purchase orders after the implementation of the policy (April 2021 to November 2022). Interrupted time series (ITS) analysis was performed to evaluate the effects of the transparent online open procurement policy on the prices, volumes, and total costs of the purchase orders. Results: After implementation of the transparent online open procurement policy, the average price of purchased medicines showed a declining trend by 0.012 Yuan per month, while the total volume of purchase orders declined at a rate by 1.741 million per month measured by the smallest formulation units and the total costs of the purchase orders decreased at a rate by 5.525 million Yuan per month. Conclusion: The transparent online open procurement policy resulted in reduced prices, lowered volumes, and lowered total costs of purchased orders of medicines.

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Correlated transparent conducting oxides (TCOs) have gained great attention, because of their unique combination of transparency and metallic character. SrVO3 (SVO) was identified as a high-performance TCO in the visible range. Few studies have investigated band structure engineering through chemical doping to enhance the optical properties of SVO. Here, we use two different strategies by exploiting the band-filling and width of the bands derived from Vanadium to tune the screened plasma frequency ωp* and the interband transition Ep-d energy, corresponding to the optical transparency window edges. For control of the band-filling strategy, it is found that Titanium doped SVO has a wide transparency window, but such a composition does not maintain the high electrical conductivity required for TCO applications. Concerning the bandwidth strategy, the doping of SrVO3 by Calcium shows that ωp* remains located in the IR range (1.12 eV), while Ep-d is blue-shifted into the UV region (3.43 eV) due to reinforced electronic correlations. By an appropriate choice of dopant, we successfully increased the size of the transparency window by around 11% from 1.94 eV (SVO) to 2.30 eV (Calcium-doped SVO), while retaining high conductivity of around 2.30 × 104 (S·cm-1) and high charge carrier density of 2.93 × 1022 cm-3.

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Patient demand for procedures has increased in the evolving landscape of cosmetic dermatology. This has been fueled, in part, by social media and the growing normalization of cosmetic enhancements; however, this has led some patients to have potentially unrealistic expectations, placing undue pressure on dermatologists to meet these often unrealizable demands. This pressure is further exacerbated by patients who are seen as difficult, demanding, and time-consuming and who may require extensive counseling. Physicians may adopt dynamic or differential pricing strategies to offset the additional time and effort these patients require. We discuss the ethical concerns surrounding these pricing strategies in the cosmetic sphere, highlight the importance of transparency in pricing, and offer suggestions to promote clarity and fairness in cosmetic dermatology practices.

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Human visual function depends on the biological lens, a biconvex optical element formed by coordinated, synchronous generation of growth shells produced from ordered cells at the lens equator, the distal edge of the epithelium. Growth shells are comprised of straight (St) and S-shaped (SSh) lens fibers organized in highly symmetric, sinusoidal pattern which optimizes both the refractile, transparent structure and the unique microcirculation that regulates hydration and nutrition over the lifetime of an individual. The fiber cells are characterized by diversity in composition and age. All fiber cells remain interconnected in their growth shells throughout the life of the adult lens. As an optical element, cellular differentiation is constrained by the physical properties of light and its special development accounts for its characteristic symmetry, gradient of refractive index (GRIN), short range transparent order (SRO), and functional longevity. The complex sinusoidal structure is the basis for the lens microcirculation required for the establishment and maintenance of image formation.

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OBJECTIVE: We aimed to compare the results of phase III and IV clinical trials examining drugs to treat multiple sclerosis (MS) registered at ClinicalTrials.gov to those published in peer-reviewed journals. METHODS: After identifying trials registered at ClinicalTrials.gov, consecutive searches were conducted in PubMed, EMBASE and Google Scholar for matching publications. Information regarding participants and efficacy and safety results was extracted and compared. The degree of consistency was classified as 'concordant', 'discrepant' or 'not comparable'. The Kaplan-Meier method was used to model time to reporting. RESULTS: In total, 65 trials were appraised. The median time from completion to reporting was shorter for ClinicalTrials.gov (16.4 vs 27.3 months; p = 0.010). Information availability was generally higher in journals except for serious adverse events (SAEs) (86.2% vs 100.0%, p = 0.029) and their description (78.2% vs 100.0%, p < 0.001). However, 45 trials had at least one reporting discrepancy (69.2%). Three studies omitted one or more primary outcomes in the matching journal publication. Regarding safety results, the lowest consistencies were found for causes of death (60.0%) and description of SAEs (27.9%). CONCLUSION: Consulting both ClinicalTrials.gov and journals increases the accessibility to MS clinical trial results. Some data were frequently missing or disagreed between sources, raising concerns about transparency and generalizability of results.

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Background: In the past, clinical trials run in India have been the subject of criticism. Among other steps to improve the trial ecosystem, for some time the government limited the number of trials that a Principal Investigator (PI) could run to three at a time. We were interested to know how many trials PIs in India tend to run at a time. Methods: We accessed the 52,149 trial records hosted by the Clinical Trials Registry-India in April 2023. Of these, we shortlisted trials that had run in India, were interventional, and involved certain interventions such as drug, biological etc. We used multiple parameters, such as email ID, phone number etc. to determine whether one name always represented the same PI and whether two names corresponded to the same PI. We then determined how many trials each PI had run. Results: We found that 3,916 unique PI names were associated with 6,665 trials. Of these, 2,963 (75.7%) PIs had run a single study. Only 251 (6.4%) had run more than three trials. A mere 14 PIs had run 20 or more trials. The 14 PIs were affiliated with local pharma companies (6), local or global contract research organizations (4), multinational pharma companies (3) and the Central Council for Research in Homeopathy (1). The maximum number of trials run by a single PI was 108. Of these, the largest number run in a single year, 2022, was 53. Conclusion: Each PI name needs to be connected to a unique ID that does not change with time, so that it is easier to track the number of trials that a given PI has run. The number of studies run by a given PI at a given time must not be excessive and needs to be monitored more actively. The government needs to consider whether a cap on the number of trials that a PI runs at a time is required and what infrastructure needs to be in place to facilitate higher numbers of trials. Trial registry records need to be updated more regularly. Other countries may wish to do likewise.

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This study focuses on creating a superhydrophobic, durable, and exceptionally transparent coating with dual-scale roughness by naturally formed raspberry-like particles. This approach facilitates the management of surface roughness at both single and dual scales through variations in surface functionalization temperature. We illustrated that adjusting the temperature of organosilanes functionalization on the surface allows for various reactions, such as the direct grafting of metallic precursors or their polymerization on the surface, resulting in the formation of large raspberry-like particles. We investigated the impact of nanoparticle concentration, functionalization duration, and reaction temperature on surface properties. Our results reveal that a concentration of 1.5 % SiO2 nanoparticles, combined with surface functionalization using TCMS for 4 h at 3 °C, provides the optimal conditions for creating a surface that combines superhydrophobicity, transparency, and acceptable durability. The resulting surface exhibits an impressive contact angle of 158.9°, a sliding angle of 2°, and a transmittance rate of 82 %. Furthermore, the coating demonstrates remarkable resistance to abrasion for up to 35 cycles and can withstand temperatures up to 280 °C. It also offers enhanced protection against UV radiation for 50 h and improved resistance to sand abrasion for up to 30 s, enduring bombardment pressures of up to 6 bars. Moreover, the coating presents several advantages in terms of surface cleaning.

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Introduction: Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. Methods: We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. Results: We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Discussion: Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

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The principles of social justice, equity, diversity, inclusion (JEDI) have received increasing attention in behavior analysis circles, but the conversation has largely centered on implications for applied behavior analysis practice and research. It may be less clear to researchers who conduct basic and translational research how JEDI principles can inform and inspire their work. This article synthesizes publications from behavior analysis and other scientific fields about tactics of JEDI-informed research. We organized this scholarship across five stages of research from developing the research question to sharing findings and curated sources for an audience of behavioral science researchers. We discuss reflexive practice, representation, belongingness, participatory research, quantitative critical theory, and open science, among other topics. Some researchers may have already adopted some of the practices outlined, some may begin new practices, and some may choose to conduct experimental analyses of JEDI problems. Our hope is that those actions will be reinforced by the behavior analysis scientific community. We conclude by encouraging the leadership of this journal to continue to work toward the structural changes necessary to make the experimental analysis of behavior just, equitable, diverse, and inclusive.